Robust equivalent consumption-based controllers for a dual-mode diesel parallel HEV

Finesso, Roberto; Spessa, Ezio; Venditti, Mattia

doi:10.1016/j.enconman.2016.08.021

Cited by 16 publications

(9 citation statements)

References 37 publications

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“….. ω r1 to characterize, and the vehicle jerk has the same direction as .. ω r1 . According to Equations (2), (3), (6) and 13, the following equation can be obtained:…”

Section: Jerk Analysismentioning

confidence: 99%

“…In the field of HEV research, existing studies mostly specified fuel economy as the primary research objective [3,4], and several have focused on the emission characteristics of HEVs [5,6]. However, both the fuel economy and emission characteristic of HEVs are associated with relatively low-bandwidth nature, while the higher bandwidth nature, vehicle riding comfort, is often ignored by most existing HEV strategies [7].…”

Section: Introductionmentioning

confidence: 99%

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Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

et al. 2018

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Given its highly coupled multi-power sources with diverse dynamic response characteristics, the mode transition process of a power-split Hybrid Electric Vehicle (HEV) can easily lead to unanticipated passenger-felt jerks. Moreover, difficulties in parameter estimation, especially power-source dynamic torque estimation, result in new challenges for jerk reduction. These two aspects entangle with each other and constitute a complicated coupling problem which obstructs the realization of a valid anti-jerk method. In this study, a vehicle dynamics model with reference to a data-driven modeling method is first established, integrating a full-time artificial neural network engine dynamic model that can accurately predict engine dynamic torque. Then the essential reason for the occurrence of vehicle jerks in real driving conditions is analyzed. Finally, to smooth the mode transition process, a more practical anti-jerk strategy based on power-source torque changing rate limitation (TCRL) is proposed. Verification studies indicate that the data-driven vehicle dynamics model has enough accuracy to reflect the vehicle dynamic characteristics, and the proposed TCRL strategy could reduce the vehicle jerk by up to 85.8%, without any sacrifice of vehicle performance. This research provides a feasible method for precise modeling of vehicle dynamics and a reference for improving the riding comfort of hybrid electric vehicles.

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“….. ω r1 to characterize, and the vehicle jerk has the same direction as .. ω r1 . According to Equations (2), (3), (6) and 13, the following equation can be obtained:…”

Section: Jerk Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

et al. 2018

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“…Both types have been extensively studied. The researches have covered various aspects of these strategies, including the state of the art of energy management strategy, general formalization of the energy management problem, and characteristics and control effects of different strategies [32][33][34][35][36][37][38][39]. In an industrial application, rule-based strategies are more widespread.…”

Section: Rule-based Energy Managementmentioning

confidence: 99%

“…Morteza and Mehdi [36] developed a new energy management strategy for power splitting in HEVs using a multi-input fuzzy logic controller to further improve their fuel economy, tailpipe emissions, and performance in various driving cycles. Finesso et al [37] applied an equivalent consumption minimization strategy to identify the optimal control strategy for a parallel HEV (P2 HEV). Li et al [38] introduced a downshift strategy, and their hardware-inthe-loop simulation showed that this strategy can improve the energy conservation of HEV regenerative braking by 10%-32%.…”

Section: Introductionmentioning

confidence: 99%

Development Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy

et al. 2018

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Energy conservation and emissions reduction have become increasingly significant for automobiles due to the severity of the current energy situation. Hybrid electric vehicle (HEV) technology is one of the most promising solutions. This study investigated the total efficiency of a HEV powertrain. To improve the total efficiency, the engine should be regulated to work at its highest efficiency and drive the wheels directly as much as possible. To accomplish this, we developed an energy management strategy based on the direct drive area (DDA) of the engine's efficiency map. Several typical HEV models were built to compare the fuel consumption using DDA and rule-based strategies. Furthermore, the function of the HEV transmission system with DDA was considered. The transmission in a HEV should regulate the engine to work at its highest efficiency as much as possible, which is rather different than the regulation in an internal combustion engine vehicle. The functional change may lead to transmission systems with fewer gears but optimal gear ratios. If this trend is realized, the manufacturing cost of HEVs could be largely reduced.

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“…Vehicle electrification supplies an efficient path to reduce the dependence on fossil fuel energy, and electric vehicles (EVs) and hybrid EVs (HEV) dominate the development trend among all feasible solutions [1,2]. Moreover, 2 plug-in HEVs (PHEVs) are also widely adopted due to the operation mode incorporation of EVs and HEVs, which, in turn, complicates the design of control algorithms, including energy allocation scheme among different energy sources (usually internal combustion engine (ICE) and lithium-ion battery pack).…”

Section: Introductionmentioning

confidence: 99%

Data-driven based eco-driving control for plug-in hybrid electric vehicles

Liu

Zhang

et al. 2021

Journal of Power Sources

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With the development of connected and automated vehicles, eco-driving control is reckoned to generate unprecedented potential on energy-saving in electrified powertrain. In this paper, a data-driven based eco-driving control strategy with efficient computation capacity is proposed for plug-in hybrid electric vehicles to achieve approximate optimal energy economy. An efficient hierarchical optimal control scheme is designed to mitigate the massive computational cost during velocity optimization and powertrain control. A data-driven optimal energy consumption cost model and an optimal battery current model are respectively constructed via two neural networks and served as the critic model and the system model during velocity optimization. Furthermore, the neural networkbased dynamic programming is exploited to optimize the vehicle velocity by merging the data-driven models and Bellman optimality principle. The simulation results demonstrate that the proposed method can remarkably improve fuel economy by up to 16.7% in complicated driving conditions, compared with conventional sequential optimization methods. Furthermore, the data-driven control scheme can drastically improve the computational efficiency with slight sacrifice on fuel economy, compared with the optimum benchmark.

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Robust equivalent consumption-based controllers for a dual-mode diesel parallel HEV

Cited by 16 publications

References 37 publications

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Jerk Analysis of a Power-Split Hybrid Electric Vehicle Based on a Data-Driven Vehicle Dynamics Model

Development Trends of Transmissions for Hybrid Electric Vehicles Using an Optimized Energy Management Strategy

Data-driven based eco-driving control for plug-in hybrid electric vehicles

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