Design and Control Co-Optimization for Hybrid Powertrains: Development of Dedicated Optimal Energy Management Strategy

Zhao, Jianning; Sciarretta, Antonio

doi:10.1016/j.ifacol.2016.08.042

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…As for the light-duty engines, the analytic model is expressed as where P ef is the chemical power of burned fuel, P e0 is the friction power in function of engine speed v e , P e is the engine brake power, P ec is the engine corner power of the best efficiency that is in function of engine speed v e , k e is the indicated efficiency in function of engine speed v e when engine power is no greater than engine corner power, and k f is the constant indicated efficiency when engine power is greater than engine corner power [10,11]. Concerning the heavy-duty engines, (for example, the large turbocharged diesel engines), the engine parametric model is a quadratic function expressed as…”

Section: Internal Combustion Engine (Eng)mentioning

confidence: 99%

“…where P be is the electrochemical power, P bt is the terminal power that is positive in discharging phase and negative in charging phase, k i ¼ k i ðN bc Þði ¼ b0; b1; b2Þ are the coefficients as a function of battery cell number N bc [10,12]. The analytic model of battery is developed at room temperature.…”

Section: Battery (Bat)mentioning

confidence: 99%

“…where P me is the electric power, P mm is the mechanical power at motor shaft, and coefficients k i (i = m0, … , m4) are identified based on mapped data of EMGs [10,12].…”

Section: Electric Motor/generator (Emg)mentioning

confidence: 99%

“…Combining Equation (1) for light-duty vehicles (or (2) for heavy-duty vehicles), Equations (6), (7), and (16), the closed-form Hamiltonian can be found in [10,12]. Then, Selective Hamiltonian Minimization (SHM) is developed based on analytic solutions to the closed-form Hamiltonian.…”

Section: Solutions Based On Pontryagin's Minimum Principlementioning

confidence: 99%

“…In [9], the analytic technique is applied to DP, hence leading to orders of magnitude reduction of computation time. The analytic technique is also applied to PMP in [10]. The method, namely Selective Hamiltonian Minimization (SHM), reduces computation time by orders of magnitude for parallel and series HEVs.…”

Section: Introductionmentioning

confidence: 99%

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GRAB-ECO for Minimal Fuel Consumption Estimation of Parallel Hybrid Electric Vehicles

Zhao

Sciarretta

Eriksson

2017

Oil & Gas Science and Technology - Rev. IFP Energies nouvel

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-As a promising solution to the reduction of fuel consumption and CO 2 emissions in road transport sector, hybrid electric powertrains are confronted with complex control techniques for the evaluation of the minimal fuel consumption, particularly the excessively long computation time of the design-parameter optimization in the powertrain's early design stage. In this work, a novel and simple GRaphical-Analysis-Based method of fuel Energy Consumption Optimization (GRAB-ECO) is developed to estimate the minimal fuel consumption for parallel hybrid electric powertrains in light-and heavy-duty application. Based on the power ratio between powertrain's power demand and the most efficient engine power, GRAB-ECO maximizes the average operating efficiency of the internal combustion engine by shifting operating points to the most efficient conditions, or by eliminating the engine operation from poorly efficient operating points to pure electric vehicle operation. A turning point is found to meet the requirement of the final state of energy of the battery, which is charge-sustaining mode in this study. The GRAB-ECO was tested with both light-and heavy-duty parallel hybrid electric vehicles, and validated in terms of the minimal fuel consumption and the computation time. Results show that GRAB-ECO accurately approximates the minimal fuel consumption with less than 6% of errors for both light-and heavy-duty parallel hybrid electric powertrains. Meanwhile, GRAB-ECO reduces computation time by orders of magnitude compared with PMP-based (Pontryagin's Minimum Principle) approaches.

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Section: Internal Combustion Engine (Eng)mentioning

confidence: 99%

Section: Battery (Bat)mentioning

confidence: 99%

“…where P me is the electric power, P mm is the mechanical power at motor shaft, and coefficients k i (i = m0, … , m4) are identified based on mapped data of EMGs [10,12].…”

Section: Electric Motor/generator (Emg)mentioning

confidence: 99%

Section: Solutions Based On Pontryagin's Minimum Principlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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