An Evaluated Review of Powertrain Control Strategies for Hybrid Electrical Vehicles

Helbing, Maximilian; Uebel, Stephan; Tempelhahn, Conny; Bäker, Bernard

doi:10.1007/s38314-015-0537-6

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The electric power of the battery P BAT (16) is transferred by the static EF λ 0 into as virtual fuel power. An equivalent fuel power P λ is gained from the addition of the real and virtual fuel power Energies 2023, 16, 5171 9 of 29 (17). For the calculated equivalent fuel power, the minimum is determined to define the optimal operation mode for each time step of the driving cycle.…”

Section: Ecmsmentioning

confidence: 99%

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Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

2023

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To fulfil the CO2 emission reduction targets of the European Union (EU), heavy-duty (HD) trucks need to operate 15% more efficiently by 2025 and 30% by 2030. Their electrification is necessary as conventional HD trucks are already optimized for the long-haul application. The resulting hybrid electric vehicle (HEV) truck gains most of the fuel saving potential by the recuperation of potential energy and its consecutive utilization. The key to utilizing the full potential of HEV-HD trucks is to maximize the amount of recuperated energy and ensure its intelligent usage while keeping the operating point of the internal combustion engine as efficient as possible. To achieve this goal, an intelligent energy management strategy (EMS) based on ECMS is developed for a parallel HEV-HD truck which uses predictive discharge of the battery and adaptive operating strategy regarding the height profile and the vehicle mass. The presented EMS can reproduce the global optimal operating strategy over long phases and lead to a fuel saving potential of up to 2% compared with a heuristic strategy. Furthermore, the fuel saving potential is correlated with the investigated boundary conditions to deepen the understanding of the impact of intelligent EMS for HEV-HD trucks.

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Section: Ecmsmentioning

confidence: 99%

“…The parameterization of the rules can be done by observations of the optimal system behavior [3,12,13]. For a detailed overview of EMSs and their implementation, see [14][15][16][17][18]. To obtain high quality in an EMS, the specific boundary conditions of the target vehicle should be considered during its development process.…”

Section: Introductionmentioning

confidence: 99%

Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

2023

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“…So that large torque gradients, frequent gear changes, start and stop intervals can be avoided. Modern powertrain control systems include a large number of different control approaches and combinations of these [44].…”

Section: Overview Of Smart Evmentioning

confidence: 99%

A Review on Driving Control Issues for Smart Electric Vehicles

Haque

Rahman²,

Islam

et al. 2021

IEEE Access

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Smart electric vehicles (EVs) are attractive because of their clean, zero-emission, low impact on the environment whilst providing a safer and smoother riding experience. To provide the latter, driving control requires appropriate systems and algorithms to optimize smart vehicle performance, maximize vehicle stability and protection, minimize accident probability, heighten driving comfort, and optimize transportation costs. Despite advancements in these areas, the realization of optimal smart EVs still requires considerable effort. This paper reviews driving control systems and algorithms for smart EVs, including the advanced driving assistant system, implementation of sensors, vehicle dynamics, and control algorithms. The major contribution of this review is to identify promising work to assist researchers with the most advanced trends in this area for prospective regulations. INDEX TERMSSmart Electric Vehicles (EVs), Driving Control Systems (DCS), Advanced Driving Assistance System (ADAS), Algorithms.

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“…In rule-based methods, rules are achieved through engineers' knowledge in addition to trial and error. Since the rules are driven without any prior knowledge of driving cycles, rule-based methods are failed to address the optimal EMS [5]. In contrast, optimization-based methods derive optimal control inputs for powertrain components with the goal of mostly improving fuel economy.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Ostadian

Ramoul

Biswas

et al. 2020

IEEE Open J. Veh. Technol.

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By increasing the electrified vehicles trend, it is crucial to have a survey of what is done before and what is the open challenge for improving this industry. Powertrain electrification has heightened the need for an energy management strategy, which has been a continuing concern in the development of electrified vehicles. An energy management control unit manages power flow between different energy sources in an electrified powertrain that directly affects vehicle performance. There are inadequate numbers of studies on the most recent energy management strategies, which are compatible with real-world scenarios. Intelligent energy management strategies requirements are discussed in detail, and it is categorized to principle-based, data-driven, and composite-based methods. Besides, enabling technologies for implementing an energy management system with a comparison of different controller chips are described to give readers an experimental view. Future trends and existing challenges are presented, which generate fresh insight into energy management strategies. Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture.

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An Evaluated Review of Powertrain Control Strategies for Hybrid Electrical Vehicles

Cited by 7 publications

References 12 publications

Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

Advanced ECMS for Hybrid Electric Heavy-Duty Trucks with Predictive Battery Discharge and Adaptive Operating Strategy under Real Driving Conditions

A Review on Driving Control Issues for Smart Electric Vehicles

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

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