A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries

Liu, Xintian; Deng, Xiaozhao; He, Ye; Zheng, Xinxin; Zeng, Guojian

doi:10.3390/en13010121

Cited by 15 publications

(9 citation statements)

References 21 publications

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“…Statistical weights were estimated for the covariance. Based on the vehicle system derived above, the observation equations, and the principle of UT, the unscented Kalman filter procedure is as follows [21][22][23][24][25][26]:…”

Section: Unscented Kalman Filter Frameworkmentioning

confidence: 99%

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

et al. 2021

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Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

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Section: Unscented Kalman Filter Frameworkmentioning

confidence: 99%

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

et al. 2021

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“…All sigma points are then transmitted via nonlinear model functions, providing an a priori approximation of the states and output signal. Depending on their statistics, the mean and covariance of those variables are determined [5]. Cell balancing is a critical feature of every BMS since it ensures that each cell has the same amount of charge and so maximizes the overall energy available in the pack.…”

Section: Unscented Kalman Filtermentioning

confidence: 99%

Modeling and Simulation of Battery Management System (BMS) for Electric Vehicles

Bhovi¹,

Ranjith²,

Sachin³

et al. 2021

JUSST

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Electric cars have evolved into a game-changing technology in recent years. A Battery Management System (BMS) is the most significant aspect of an Electric Vehicle (EV) in the automotive sector since it is regarded as the brain of the battery pack. Lithium-ion batteries have a large capacity for energy storage. The BMS is in charge of controlling the battery packs in electric vehicles. The major role of the BMS is to accurately monitor the battery’s status, which assures dependable operation and prolongs battery performance. The BMS’s principal job is to keep track, estimate, and balance the battery pack’s cells. The major goals of this work are to keep track of battery characteristics, estimate SoC using three distinct approaches, and balance cells. Coulomb Counting, Extended Kalman Filter, and Unscented Kalman Filter are the three algorithms that will be implemented. Current is used as an input parameter to implement the coulomb counting method. In contrast to voltage and temperature, the current value is taken into account by the Extended and Unscented Kalman Filters. To calculate the state transition and measurement update matrix, these parameters are considered. This matrix will then be used to calculate SoC. Results of all the algorithms will be comparatively analyzed. MATLAB R2020a software is used for the simulation of different algorithms and SoC calculation. Three states of BMS are considered and they are Discharging phase, the Standby/resting phase, and the Charging phase. At the beginning of the Simulation, the SoC values of the cells were 80%. At the end of simulation maximum values of SoC of Coulomb counting, Extended Kalman Filter (EKF), and Unscented Kalman Filter (UKF) reached are 100%, 98.74%, and 98.46% respectively. After SoC Estimation, Cell balancing is also performed over 6 cells of the battery pack.

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“…However, as for which specific enterprises complete which link of work, and the benefit distribution of enterprises with different roles is not in the scope of this study. Second, in order to simplify the calculation, the battery capacity retention rate is used to measure the overall performance of the battery and the only judgment condition of battery state of health, service life, and whether it should be transferred to the next link [40,41]. The time point of decommissioning of the battery from the whole vehicle and the judgment condition of the ultimate scrapping are all expressed by battery performance; therefore, all expressions related to the battery service life are also unified to the form of capacity retention rate in the process of model calculation.…”

Section: Economic Benefit Evaluation Model Of Echelon Utilizationmentioning

confidence: 99%

Research on the Critical Issues for Power Battery Reusing of New Energy Vehicles in China

Liu

Hao

et al. 2020

Energies

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With the rapid development of new energy vehicles (NEVs) industry in China, the reusing of retired power batteries is becoming increasingly urgent. In this paper, the critical issues for power batteries reusing in China are systematically studied. First, the strategic value of power batteries reusing, and the main modes of battery reusing are analyzed. Second, the economic benefit models of power batteries echelon utilization and recycling are constructed. Finally, the economic benefits of lithium iron phosphate (LIP) battery and ternary lithium (TL) battery under different reusing modes are analyzed based on the economic benefit models. The results show that when the industrial chain is fully coordinated, LIP battery echelon utilization is profitable based on a reasonable scenario scheme. However, the multi-level echelon utilization is only practical under an ideal scenario, and more attention should be paid to the first level echelon utilization. Besides, the performance matching of different types of batteries has a great impact on the echelon utilization income. Thus, considering the huge potentials of China’s energy storage market, the design of automobile power batteries in the future should give due consideration to the performance requirements of energy storage batteries. Moreover, the TL battery could only be recycled directly, while the LIP has the feasibility of echelon utilization at present. At the same time, it will strengthen the cost advantage of the LIP battery, which deserves special attention.

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A Dynamic State-of-Charge Estimation Method for Electric Vehicle Lithium-Ion Batteries

Cited by 15 publications

References 21 publications

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Modeling and Simulation of Battery Management System (BMS) for Electric Vehicles

Research on the Critical Issues for Power Battery Reusing of New Energy Vehicles in China

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