In this paper, the battery management system of a hybrid electric vehicle (PHEV) is analyzed and studied. A vehicle battery management system suitable for PHEV is designed. The dynamic estimation of soc, the processin...
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ISBN:
(纸本)9798350310801
In this paper, the battery management system of a hybrid electric vehicle (PHEV) is analyzed and studied. A vehicle battery management system suitable for PHEV is designed. The dynamic estimation of soc, the processing mode of signal, the wake up and exit process of BMS and the auxiliary contact detection of relay are studied. Based on the theory of multiple time scales, the operating state and life of on-board battery are considered comprehensively, and the changes of soc and SOH in dynamic operation of lithium-ion battery are predicted accurately, so that lithium-ion battery can be recycled more effectively in the process of vehicle development.
The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (soc) more accurately, the artificial fi...
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The non-linear characteristic of power lithium battery restricts the establishment of accurate battery models. To overcome this problem and estimate the battery state of charge (soc) more accurately, the artificial fish swarm algorithm-back propagation (AFSA-BP) neural network structure was designed based on AFSA and BP neural network theory. According to the test parameters of power lithium battery, the related mathematical model was established. The flow charts of optimising BP neural network with AFSA algorithm and estimating soc value by AFSA-BP algorithm are given. The specific implementation steps are elaborated. Using the 48 V, 50 Ah lithium iron phosphate (LiFePO4) power battery as experimental object, through the periodic charging and discharging experiments and software simulation, the correctness, validity and accuracy of the application of AFSA-BP neural network in estimating soc value of the power lithium battery are verified.
In this paper, a mixed algorithm is proposed to overcome the limitations of the conventional algorithms, which cannot be applied in various driving patterns of drivers. The proposed algorithm based on the coulomb coun...
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In this paper, a mixed algorithm is proposed to overcome the limitations of the conventional algorithms, which cannot be applied in various driving patterns of drivers. The proposed algorithm based on the coulomb counting method is mixed with reset algorithms that consist of the enhanced OCV reset method and the DCIR iterative calculation method. It has many advantages, such as a simple model structure, low computational overload in various profiles, and a low accumulated soc error through the frequent soc reset. In addition, the enhanced parameter based on a mathematical analysis of the second-order RC ladder model is calculated and is then applied to all of the methods. The proposed algorithm is verified by experimental results based on a 27-Ah LiPB. It is observed that the soc RMSE of the proposed algorithm decreases by about 9.16% compared to the coulomb counting method.
In this paper, a mixed algorithm is proposed to improve socestimation accuracy for large-capacity Li-ion battery by using advantages of coulomb counting method, DCIR reset method, and enhanced OCV reset method. Becau...
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ISBN:
(纸本)9788957082546
In this paper, a mixed algorithm is proposed to improve socestimation accuracy for large-capacity Li-ion battery by using advantages of coulomb counting method, DCIR reset method, and enhanced OCV reset method. Because each method has drawbacks of accumulated soc error during the operation in EVs, the optimal mixed algorithm is presented. Also, the weighted current value IDC based on the mathematical analysis of the second-order RC ladder model is calculated and then it is applied to methods respectively. The proposed algorithm is verified by the experimental results based on the 27 Ah LiPB. As a result, the soc RMSE of the proposed algorithm is decreased about 2.22% by compared with the coulomb counting method.
Lithium-ion battery as an efficient, sustainable, and clean energy for electric vehicles (EVs) and smart devices becomes more popular with the worldwide demand for reduction of greenhouse gas emission. In all kinds of...
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Lithium-ion battery as an efficient, sustainable, and clean energy for electric vehicles (EVs) and smart devices becomes more popular with the worldwide demand for reduction of greenhouse gas emission. In all kinds of applications, an accurate real-time estimation for state of charge (soc) of battery is necessary. Some conventional methods usually need to sample both battery currents and voltages. This article presents a novel soc estimation algorithm without current detection. This algorithm just acquires the port voltages of cell to calculate the open-circuit voltage (OCV) which is related to soc. By extracting a large number of battery voltages based on a step response, some important parameters that can track battery working process are determined. In order to verify the algorithm feasibility and accuracy, it has been tested on a commercial common field-programmable gate array (FPGA) in different application conditions. The algorithm accuracy is mainly limited by model accuracy and sampling sensor accuracy. The maximum error between ideal soc and calculated soc by this algorithm is within 4%, and the mean error is about 0.99%. So, this high-feasibility, accredited accuracy, easy integration, and low-cost solution has bright potential in smarter future.
On the basis of traditional single-particle model, an extended single-particle (ESP) electrochemical model that considers the influence of electrolyte phase potential on terminal voltage in the light of electrochemica...
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On the basis of traditional single-particle model, an extended single-particle (ESP) electrochemical model that considers the influence of electrolyte phase potential on terminal voltage in the light of electrochemical characteristics of lithium ion battery is designed. The linear decreasing weight particle swarm algorithm is adopted to identify the key parameters of the ESP electrochemical model to reduce the effect of parameter identification error on the accuracy of state-of-charge (soc) estimation. And an ESP-model-based extended Kalman filter (EKF) algorithm which can compensate the error caused by the simplified solution and random noise by feedback control is also proposed. The simulation results demonstrate that EKF algorithm reduce calculation errors, parameter measurement noises and increase accuracy of socestimation for lithium ion battery. Finally, the Charge/discharge test using 2300mAh LiFePO4 battery is carried out at 3C FUDS and comparison of experimental and simulated results show that ESP-model-based EKF algorithm using for socestimation has good accuracy and robustness.
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