The Development and Verification of a Novel ECMS of Hybrid Electric Bus

Wang, Jun; Wang, Qingnian; Wang, Pengyu; Zeng, Xiaohua

doi:10.1155/2014/981845

Cited by 10 publications

(10 citation statements)

References 29 publications

(29 reference statements)

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“…In consideration of the relationship between the equivalent consumption minimization and the equivalent factor, the optimal equivalent factor of charge and discharge in urban, suburban, and high-speed conditions is obtained. According to the method provided in Simona et al, 2 Wang et al, 23 Paganelli et al, 29 the exhaustive method is applied to calculate the optimal equivalent factor for obtaining the lowest equivalent fuel consumption under different types of driving cycles. The optimal equivalent factors of the HEV in this paper are shown in Table 4.…”

Section: A-ecms With Prediction and Recognitionmentioning

confidence: 99%

“…19,20 Other researchers proposed the real-time A-ECMS, which is based on driving information, to reduce equivalent fuel consumption under different road conditions. [21][22][23] However, for the data computing capabilities of existing vehicle processors, obtaining time-varying discrete tables and reoptimization are complex. In addition, with the improvement in calculation accuracy, the calculation time will increase greatly, which is the main defect of the A-ECMS.…”

Section: Introductionmentioning

confidence: 99%

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A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

Liu

Shi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper proposes an adaptive control strategy of fuel consumption optimization for hybrid electric vehicles (HEVs). The strategy combines a moving-horizon-based nonlinear autoregressive (NAR) algorithm, a backpropagation (BP) neural network algorithm, and an equivalent consumption minimization strategy (ECMS) method to reduce energy consumption. The moving-horizon-based NAR algorithm is applied to predict the short future driving cycle. The BP neural network algorithm is employed to recognize the driving cycle types, which provides the basis for the adaptive ECMS. Based on the abovementioned approach, the power split of the fuel and electric system is determined in advance, and the optimal control of energy efficiency is achieved. A driving experiment platform is established, taking a synthetic driving cycle composed of several standard driving cycles as the target cycle, and the control strategy is tested by the driver’s real operation. The results indicate that, compared with the basic ECMS, the A-ECMS with moving-horizon-based driving cycle prediction and recognition has better SOC (state of charge) retention and reduces the fuel consumption of the engine by 3.31%, the equivalent fuel consumption of the electric system by 0.9 L/100 km and the total energy consumption by 1 L/100 km. Adaptive ECMS based on driving cycle prediction and recognition is an effective method for the energy management of HEVs.

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Section: A-ecms With Prediction and Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

Liu

Shi

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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show abstract

“…Actually, the EF adaptation law based on SOC feedback mainly includes P controller (Equation [6]), PI controller (Equation [7]), discrete controller (Equation [8]), and improved discrete controller (Equation [9]). 33,37,40,48 ΔSOC…”

Section: Adaptive Law and Assumptionmentioning

confidence: 99%

“…On the contrary, it indicates that the battery energy is excessive and the EF needs to be decreased. Actually, the EF adaptation law based on SOC feedback mainly includes P controller (Equation ]), PI controller (Equation ]), discrete controller (Equation ]), and improved discrete controller (Equation ]) 33,37,40,48

normalΔ italicSOC ((), t) = ((), {SOC}_{ref} - italicSOC ((), t)),

s ((), t) = s_{0} + K_{p} normalΔ italicSOC ((), t),

s ((), t) = s_{0} + K_{p} normalΔ italicSOC ((), t) + K_{i} \sum_{0}^{t} normalΔ italicSOC ((), t),

s ((), t + 1) = s ((), t) + K_{p} normalΔ italicSOC ((), t) ((), t = italickT, k = 1, 2, 3, \dots),

s ((), k + 1) = \frac{(s_{k - 1} + s_{k})}{2} + K_{p} normalΔ italicSOC ((), t) ((), t = italickT, k = 1, 2, 3, \dots),

…”

Section: Adaptive Equivalent Consumption Minimization Strategymentioning

confidence: 99%

“…31 To cope with these issues, several adaptive ECMSs (A-ECMSs) have been conducted. [32][33][34] There were three methods for the automatic adjustment of EF, namely, offline calculating or predicting optimal EF by using historical driving cycle information or energy flow, 35 adjusting EF through SOC deviation feedback controller, [36][37][38] and adjusting EF in advance via predicting future driving cycle. [39][40][41] The optimal constant EF can be obtained when the first EF method is utilized.…”

Section: Introductionmentioning

confidence: 99%

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A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

2020

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The universal adaptive equivalent consumption minimization strategy (A-ECMS) has the potential of being implemented in real-time for plug-in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a longterm future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real-time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real-time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A-ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a threedimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A-ECMS, the computational time of A-ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware-in-loop results prove that the proposed novel energy management strategy A-ECMS2 has better real-time performance and less computing burden than the general A-ECMS.

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A novel power reserve and fuel power-based optimum gear shift selection technique for automated manual transmission vehicles

Noyalraj¹,

Subramaniam²

2023

AIP Conference Proceedings

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The Development and Verification of a Novel ECMS of Hybrid Electric Bus

Cited by 10 publications

References 29 publications

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

A driving-cycle predictive control approach for energy consumption optimization of hybrid electric vehicle

A novel real‐time energy management strategy for plug‐in hybrid electric vehicles based on equivalence factor dynamic optimization method

A novel power reserve and fuel power-based optimum gear shift selection technique for automated manual transmission vehicles

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