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139 results on '"Pan, Chaofeng"'

32. Optimization of Cooling Strategy for Lithium Battery Pack Based on Orthogonal Test and Particle Swarm Algorithm.

Author

Pan, Chaofeng, Jia, Zihao, Huang, Jiong, Chen, Zhe, and Wang, Jian

Subjects
*LITHIUM cells, *PARTICLE swarm optimization, *BATTERY management systems, *COOLING systems, *ALGORITHMS, *ELECTRIC vehicle batteries, *ELECTRIC batteries
Abstract

This paper describes the design and optimization of a cooling strategy based on a battery cooling system with indirect liquid-cooled plate heat exchange. The performance of the battery cooling system was analyzed using single-factor thermal simulation analysis. A cosimulation platform was built, and the rule-based multiparameter control strategy was used to design the heat dissipation logic, which was verified in the cosimulation platform. To improve the heat dissipation performance further, an orthogonal test (OT) was used to analyze the effects of different control parameters on each performance index during the battery discharge process, and the optimal combination of control parameters under a balanced multiobjective strategy was obtained. The results of the particle swarm optimization (PSO) algorithm with different weight coefficients were introduced for comparison. It was found that the OT and PSO can keep the temperature difference within the safe range. The maximum temperature was reduced by 3.57 and 4.10 K, respectively, and the average output power was increased by 11% and 9%, respectively, which indicates that the OT maintained good performance despite the significant workload reduction. The feasibility and reliability of the battery thermal management system control strategy optimized using OT were verified. [ABSTRACT FROM AUTHOR]

Published
2023
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37. Design of an economical cruise speed planning and control method for pure electric vehicles.

Author

HUANG Aibao, PAN Chaofeng, LIANG Jun, WANG Limeii, XU Xing, and HE Zhigang

Subjects
CRUISE control, SPEED, GENETIC algorithms, VEHICLE models, CRUISE industry
Abstract

In order to improve the driving range of pure electric vehicles, this paper proposes an economical cruise speed planning and control method. According to the driving environment of a vehicle, such as the following mode or the cruise mode, a short-term speed planning method based on long short-term memory (LSTM) and a long-term speed planning method based on genetic algorithm (GA) are proposed respectively. The economical cruise control system is designed based on a hierarchical structure, in which the upper layer obtains the planned speed profile by constructing the objective function of the problem to be optimized and solving it with GA, and the lower layer takes the optimal speed curve as the input and realizes distance control and speed control based on the tracking controllers. The pure electric vehicle model and the economical cruise control system are built on the joint simulation platform of CarSim and Simulink. The research results show that the introduction of speed planning can provide the vehicle with the optimal speed curve after energy optimization in multiple scenarios so as to reduce the energy consumption of electric vehicles and improve travel efficiency. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Power Batteries State of Health Estimation of Pure Electric Vehicles for Charging Process

Author

He, Zhigang, Ni, Xianggan, Pan, Chaofeng, Li, Weiquan, and Han, Shaohua

Abstract

Under different usage scenarios of various electric vehicles (EVs), it becomes difficult to estimate the battery state of health (SOH) quickly and accurately. This article proposes an SOH estimation method based on EVs’ charging process history data. First, data processing processes for practical application scenarios are established. Then the health indicators (HIs) that directly or indirectly reflect the driver's charging behavior in the charging process are used as the model's input, and the ensemble empirical mode decomposition (EEMD) is introduced to remove the noise brought by capacity regeneration. Subsequently, the maximum information coefficient (MIC)—principal component analysis (PCA) algorithm is employed to extract significant HIs. Eventually, the global optimal nonlinear degradation relationship between HIs and capacity is learned based on Bayesian-optimized Gaussian process regression (BO-GPR). Approximate battery degradation models for practical application scenarios are obtained. This article validates the proposed method from three perspectives: models, vehicles, and regions. The results show that the method has better prediction accuracy and generalization capability and lower computational cost, which provides a solution for future online health state prediction based on a large amount of real-time operational data.

Published
2024
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46. State of health estimation of battery modules via differential voltage analysis with local data symmetry method

Author

Limei Wang, Pan Chaofeng, Liang Liu, and Xiuliang Zhao

Subjects
Battery (electricity), business.product_category, State of health, 020209 energy, General Chemical Engineering, 02 engineering and technology, Interval (mathematics), Topology, Symmetry (physics), Inflection point, Electric vehicle, Linear regression, 0202 electrical engineering, electronic engineering, information engineering, Electrochemistry, business, Mathematics, Voltage
Abstract

Cyclic voltammogram (CV) and differential voltage analysis (DVA) are two effective techniques to analyze the aging mechanism and estimate the aging state of a battery. However, the effectiveness of the two methods reported previously is based on single battery cells. In this paper, a comparison of the two methods is stated, and the equivalent relation is further derived. Besides, a local data symmetry method is introduced to calculate the differential voltage (DV) curve. The DV curves calculated by the proposed method are much smoother than that by the numerical-derivative method. Based on the location interval of two inflection points in the DV curve, a new method is inferred for lithium iron phosphate (LiFePO 4 ) battery cells, and is applied to estimate the state of health (SOH) of battery modules. The applicability of the method is further verified via battery module simulation and experimental data. The results show that the DV curves fluctuate and do not overlap in the voltage plateau region due to the uneven currents flowing through each in-parallel battery cells. There is also a good linear regression of the two inflection point location interval versus battery module capacity within 2% error bounds, suggesting that the DVA method inferred from battery cells can be directly applied to battery modules.

Published
2017

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