Parameter Matching Method of a Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

Liu, Fengchen; Wang, Chun; Luo, Yan

doi:10.3390/wevj12040253

Cited by 14 publications

(12 citation statements)

References 30 publications

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“…To reduce the complexity of the power model, the efficiency of the hybrid vehicle transmission, the DC/AC converter, and the motor are all set to constants. The main characteristic parameters of the EV in this study are listed in Table 1, 31 and the required power of the vehicle can be obtained by Equation ( 1)…”

Section: Required Power Model Of the Vehiclementioning

confidence: 99%

“…The detailed parameter matching method has been summarized in our previous study, and the results are shown in Table 4. 31 The battery pack is configured with 100 cells in series, and its parallel number is 25. The series-parallel connection number of the ultracapacitor pack is 135 and 3, respectively.…”

Section: Parameter Matching Of the Battery Pack And Ultracapacitor Packmentioning

confidence: 99%

P_{req} goodbreak= \frac{V_{normala}}{3600 n_{d} n_{m} n_{t}} ((), italicmgf \cos ((), α) goodbreak+ \frac{{CAV}_{a}^{2}}{21.15} goodbreak+ italicmg \sin ((), α) goodbreak+ italicδm \frac{{italicdV}_{normala}}{dt}),

where

P_{req}

, V a , m , and

f

represents the required power, the driving speed, the vehicle mass, and the coefficient of the rolling resistance, respectively; δ denotes the correction coefficient of the rotation mass; n t , n d , and n m are the efficiency of the transmission system, the DC/AC converter, and the motor, respectively; and α and g represent the angle of the road and the gravity acceleration. The UDDS is used to simulate the driving cycle of the vehicle.…”

Section: Modeling Of the Hessmentioning

confidence: 99%

“…Consequently, reasonable parameter matching of the battery pack and ultracapacitor pack can minimize the cost, improve the efficiency, and extend the lifespan of the HESS. The detailed parameter matching method has been summarized in our previous study, and the results are shown in Table 4 31 . The battery pack is configured with 100 cells in series, and its parallel number is 25.…”

Section: Modeling Of the Hessmentioning

confidence: 99%

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A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

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SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

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Section: Required Power Model Of the Vehiclementioning

confidence: 99%

Section: Parameter Matching Of the Battery Pack And Ultracapacitor Packmentioning

confidence: 99%

P_{req} goodbreak= \frac{V_{normala}}{3600 n_{d} n_{m} n_{t}} ((), italicmgf \cos ((), α) goodbreak+ \frac{{CAV}_{a}^{2}}{21.15} goodbreak+ italicmg \sin ((), α) goodbreak+ italicδm \frac{{italicdV}_{normala}}{dt}),

where

P_{req}

, V a , m , and

f

Section: Modeling Of the Hessmentioning

confidence: 99%

Section: Modeling Of the Hessmentioning

confidence: 99%

See 2 more Smart Citations

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

Self Cite

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show abstract

“…The parallel number of ultracapacitor pack is 3 and the series number is 135. 33 In order to fully utilize the high short-time discharge rate of the ultracapacitor, the voltage of the ultracapacitor cell and pack should be maintained in the range of [1.35, 2.7] V and [182. 25,499.5] V. The core parameters of battery and ultracapacitor are summarized in Table 3.…”

Section: Modeling Of Battery and Ultracapacitor Packmentioning

confidence: 99%

Wavelet transform‐based real‐time energy management strategy of hybrid energy storage system for electric vehicle

Jiang,

Wang,

Zhang

et al. 2023

Circuit Theory & Apps

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SummaryThe peak and transient components of demand power caused by the complex and variable traffic environment could induce the accelerated degradation of the battery lifespan for electric vehicle (EV). This paper proposes a wavelet transform‐based real‐time energy management strategy (EMS) to fully exploit the advantages of the hybrid energy storage system (HESS). First, to adapt the characteristics of battery and ultracapacitor, wavelet transform is employed to decompose driving cycle into high frequency power and low frequency power. Second, since the wavelet transform is difficult to be directly implemented in real‐time control, a power prediction model including four neural network predictors is established, which are trained by the data obtained from wavelet transform to online predict the power of different frequencies. Third, the high frequency power and the low frequency power are distributed to the battery and ultracapacitor by fuzzy logic control (FLC) algorithm, which can significantly reduce the damage caused by current rapid changes into the battery. Accordingly, the battery lifespan is effectively extended because it significantly avoids the impact of rapidly changing and peak current. Finally, simulation results verify the effectiveness of wavelet transform‐based real‐time EMS, and the proposed EMS robustness against temperature is verified at different temperatures. Compared with the wavelet transform strategy (WTS), the peak currents of wavelet transform‐fuzzy logic control strategy (WTFLCS) are decreased by 26.07%, 25.66%, and 25.85% at 10°C, 25°C, and 40°C, respectively.

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Parameter Matching Optimization of All-Terrain Vehicle Battery System Considering Multi-objective Optimization

Wang

2023

Lecture Notes in Electrical Engineering

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Parameter Matching Method of a Battery-Supercapacitor Hybrid Energy Storage System for Electric Vehicles

Cited by 14 publications

References 30 publications

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wavelet transform‐based real‐time energy management strategy of hybrid energy storage system for electric vehicle

Parameter Matching Optimization of All-Terrain Vehicle Battery System Considering Multi-objective Optimization

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