Threshold-changing control strategy for series hybrid electric vehicles

Shabbir, Wassif; Evangelou, Simos A.

doi:10.1016/j.apenergy.2018.11.003

Cited by 80 publications

(44 citation statements)

References 45 publications

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“…According to previous researches, existing EMSs can be classified into rule-based strategies and optimization-based strategies. Rule-based strategies refer to a bunch of simple and robust energy distribution algorithms, which are developed based on the operation modes of energy sources [6], [7], fuzzy logic theory [8], [9] and other heuristic rules [10], [11]. However, these predefined rules or the expertise knowledge cannot fully ensure the optimal EMS performance (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

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Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

2020

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Considering the changeable driving conditions in reality, energy management strategies for fuel cell hybrid electric vehicles should be able to effectively distribute power demands under multiple driving patterns. In this paper, the development of an adaptive energy management strategy is presented, including a driving pattern recognizer and a multi-mode model predictive controller. In the supervisory level, the Markov pattern recognizer can classify the real-time driving segment into one of three predefined patterns. Based on the periodically updated pattern identification results, one set of pre-optimized control parameters is selected to formulate the multi-objective cost function. Afterwards, the desirable control policies can be obtained by solving a constrained optimization problem within each prediction horizon. Validation results demonstrate the effectiveness of the Markov pattern recognizer, where at least 94.94% identification accuracy can be reached. Additionally, compared to a single-mode benchmark strategy, the proposed multi-mode strategy can reduce the average fuel cell power transients by over 87.00% under multi-pattern test cycles with a decrement of (at least) 2.07% hydrogen consumption, indicating the improved fuel cell system durability and the enhanced fuel economy.

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Section: Literature Reviewmentioning

confidence: 99%

“…Step 3: Based on the measurements on the V-A plane, the multi-step TPMs under each driving scenario can be estimated through the conventional MC model (6). These established TPMs are stored as the offline basis for online similarity quantification.…”

Section: Offline Benchmark Transition Probability Matrices Estimationmentioning

confidence: 99%

Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

2020

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“…The simulation results indicate that compared with the adaptive equivalent consumption minimization strategy, the fuel economy of the proposed strategy is improved by 7%. Shabbir et al [21] used an optimal primary source strategy to optimize control strategy parameters. This strategy can significantly improve fuel economy compared with the rule-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Zhang

Yang

Zhou³

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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To solve the problem of unreasonable vehicle parameters caused by unknown curb mass and open-loop power system and control strategy optimization in the development of pure electric vehicles, this paper presents a parameter closed-loop optimization algorithm (COA) via unified design of system and control parameters. First, a mass closed-loop algorithm (MCA) was adopted to optimize the parameters of power systems under an unknown curb mass and its convergence was proven. Next, with energy consumption being the index function, the torque distributions of the front and rear motors were optimized by dynamic programming (DP). Additionally, vehicle power system and control strategy parameter optimization were realized by combining the MCA, DP, and genetic algorithm. Finally, two comparative optimization algorithms which are the assumed curb mass optimization algorithm (AOA) and the torque equal ratio distribution optimization algorithm (TOA) were implemented to validate the proposed algorithm. The simulation results under China light-duty vehicle test cycle-passenger car (CLTC-P) and New European Driving Cycle (NEDC) operating conditions indicate that the proposed algorithm achieves minimum energy consumption compared with the competing optimization algorithms. The energy consumption resulting from the COA is reduced by 18.98% and 6.36%, compared with those of the TOA and AOA, respectively, under CLTC-P conditions. The energy consumption of the COA is 16.57% and 6.91% lower than those of the TOA and AOA, respectively, under NEDC conditions. This algorithm can optimize the curb mass, peak powers of the motors, mass of the motors, battery energy, battery mass, and torque distribution coefficient simultaneously, and can be applied to different operating conditions.

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“…The secondary regulation technology for the constant pressure network was introduced and applied to a hybrid tactical vehicle including the series connections of an internal combustion engine (ICE), hydraulic pump, and the novel hydraulic pump/motor. The wheel-drive technology was used to improve the ground clearance of vehicle chassis [1], and the fuel economy of the vehicle could be improved by using the appropriate energy management strategy.…”

Section: Introductionmentioning

confidence: 99%

Power-Following Control Strategy of a Wheel-Drive Hydraulic Hybrid Vehicle

Zhang

Wang

et al. 2020

SV-JME

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Energy management strategy is a critical technology for improving the fuel economy of wheel-drive hydraulic hybrid vehicles. For driving, a power-following control strategy is proposed in this study by adding several working points of the engine in the optimal fuel economy power curve. For braking, the “I” curve distribution strategy based on critical braking strength zmin was adopted. A test bench was constructed according to the quarter of the prototype vehicle. Taking the typical working conditions of Federal Urban Driving Schedule (FUDS) and the selfset extra-urban driving schedule (EUDC-1) cycle condition into consideration, the energy management strategy was studied. The torque and speed of the simulated engine and pressure of the accumulator were obtained. The test fuel consumption in this research was compared with the original fuel consumption of the prototype vehicle. It was found that the proposed energy management strategy could effectively improve the fuel economy by more than 24 % under the requirement of satisfying the dynamic performance of the whole vehicle.

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Threshold-changing control strategy for series hybrid electric vehicles

Cited by 80 publications

References 45 publications

Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer

Parameter closed-loop optimization for pure electric vehicles: unified design of power system and control parameters

Power-Following Control Strategy of a Wheel-Drive Hydraulic Hybrid Vehicle

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