A brief review on key technologies in the battery management system of electric vehicles

Liu, Kailong; Li, Kang; Pan, Qiao; Zhang, Cheng

doi:10.1007/s11465-018-0516-8

Cited by 373 publications

(165 citation statements)

References 135 publications

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“…At present, various review papers are written in the field of battery management systems [20][21][22][23][24][25][26][27][28][29] and thermal management systems [30][31][32][33][34][35][36][37][38][39]. These papers mainly describe state estimation techniques and approaches how to design BMS and TMS.…”

Section: Introductionmentioning

confidence: 99%

A review on various temperature-indication methods for Li-ion batteries

et al. 2019

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Temperature measurements of Li-ion batteries are important for assisting Battery Management Systems in controlling highly relevant states, such as State-of-Charge and State-of-Health. In addition, temperature measurements are essential to prevent dangerous situations and to maximize the performance and cycle life of batteries. However, due to thermal gradients, which might quickly develop during operation, fast and accurate temperature measurements can be rather challenging. For a proper selection of the temperature measurement method, aspects such as measurement range, accuracy, resolution, and costs of the method are important. After providing a brief overview of the working principle of Li-ion batteries, including the heat generation principles and possible consequences, this review gives a comprehensive overview of various temperature measurement methods that can be used for temperature indication of Li-ion batteries. At present, traditional temperature measurement methods, such as thermistors and thermocouples, are extensively used. Several recently introduced methods, such as impedance-based temperature indication and fiber Bragg-grating techniques, are under investigation in order to determine if those are suitable for largescale introduction in sophisticated battery-powered applications.

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Section: Introductionmentioning

confidence: 99%

A review on various temperature-indication methods for Li-ion batteries

et al. 2019

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“…Consequently, a large condition matrix needs to be set up in terms of factors such as temperatures, current rates, and state of charge (SOC) ranges. The corresponding battery experiments can take several years and prohibitively high lab costs, particularly for lithium iron phosphate cells and Li 4 Ti 5 O 12 cells whose typical lives are over 3000 cycles [8,9] and 10000 cycles [10], respectively. Further, this method is not applicable to online battery applications.…”

Section: Introductionmentioning

confidence: 99%

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

et al. 2019

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This paper develops a new prediction method for the aging trajectory of lithium-ion batteries with significantly reduced experimental tests. This method is driven by data collected from two types of battery operation modes. The first type is accelerated aging tests that are performed under stress factors, such as overcharging, over-discharging and large current rates, and cover most of the battery lifespan. In the second operation mode, the same kinds of cells are aged at normal speeds to generate a partial aging profile. An accelerated aging model is developed based on the first type of data and is then migrated as a new model to describe the normalspeed aging behavior. Under the framework of Bayesian Monte Carlo algorithms, the new model is parameterized based on the second type of data and is used for prediction of the remaining battery aging trajectory. The proposed prediction method is validated on three types of commercial batteries and also compared with two benchmark algorithms. The sensitivity of results to the number of cycles is investigated for both modes. Illustrative results demonstrate that based on the normal-speed aging data collected in the first 30 cycles, the proposed method can predict the entire aging trajectories (up to 500 cycles) at a root-mean-square error of less than 2.5% for all considered scenarios. When only using the first five-cycle data for model training, such a prediction error is bounded by 5% for aging trajectories of all the tested batteries.

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“…However, few studies aimed at the energy management of the HESS for a pure HEV. Kailong et al [29] studied different techniques for a EMS with the discussion of the battery model in terms of the state of charge (SOC), the state of health, and the internal temperature of the battery. Song et al [18] compared four EMSs, which are the filtrating-based controller, the rule-based controller, the MPC, and the FLC.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

et al. 2019

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The energy storage system (ESS) is the main issue in traction applications, such as battery electric vehicles (BEVs). To alleviate the shortage of power density in BEVs, a hybrid energy storage system (HESS) can be used as an alternative ESS. HESS has the dynamic features of the battery and a supercapacitor (SC), and it requires an intelligent energy management system (EMS) to operate it effectively. In this study, a real-time EMS is proposed, which is comprised of a fuzzy logic controller-based low-pass filter and an adaptive proportional integrator-based charge controller. The proposed EMS intelligently distributes the required power from the battery and SC during acceleration. It allocates the braking energy to the SC on the basis of the state of charge. A simulation study was conducted for three standard drive cycles (New York City cycle, Artemis urban cycle, and New York composite cycle) using MATLAB Simulink. Comparative analysis of conventional and proposed EMSs was carried out. The results reveal that the proposed EMS reduced the stress, temperature, and power losses of the battery. The steady-state charging performance of the SC was 98%, 95%, and 96% for the mentioned drive cycles.

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A brief review on key technologies in the battery management system of electric vehicles

Cited by 373 publications

References 135 publications

A review on various temperature-indication methods for Li-ion batteries

A review on various temperature-indication methods for Li-ion batteries

Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method

A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles

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