A review on the key issues for lithium-ion battery management in electric vehicles

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations

Degradation diagnostics for lithium ion cells

Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries

Current density and state of charge inhomogeneities in Li-ion battery cells with LiFePO4 as cathode material due to temperature gradients

Lithium ion (Li-ion) batteries exhibit high power and energy densities, as well as high-cycle-lifetime capabilities. Li-ion cells based on LiFePO4 cathodes are promising alternatives to predominant metal oxide technologies, particularly for applications with high power and cycle lifetime requirements. This paper includes investigations on high-power LiFePO4 cells' cycle lifetime. Therefore, 3000 full charge-discharge cycles are applied to the cells with high constant current rates. Through reference testing, increasing battery impedance and a fading capacity are identified as a consequence of battery aging. Capacity fade proceeds in a way that cells' open-circuit voltages (OCVs) change only in the range of high state-of-charge (SOC). A model-based method that can detect the decreasingly utilizable capacity is outlined. The proposed method considers the changing OCV characteristics and enables the determination of capacity loss without complete charge and discharge being necessary during battery operation. By the evaluation of capacity loss information, an accelerated battery aging or even possible battery damage caused by overcharge can be avoided during battery charging scenarios.

Detection of Utilizable Capacity Deterioration in Battery Systems

The relation between batteries' state of charge (SOC) and open-circuit voltage (OCV) is a specific feature of electrochemical energy storage devices. Especially NiMH batteries are well known to exhibit OCV hysteresis, and also several kinds of lithium-ion batteries show OCV hysteresis, which can be critical for reliable state estimation issues. Electrode potential hysteresis is known to result from thermodynamical entropic effects, mechanical stress, and microscopic distortions within the active electrode materials which perform a two-phase transition during lithium insertion/extraction. Hence, some Li-ion cells including two-phase transition active materials show pronounced hysteresis referring to their open-circuit voltage. This work points out how macroscopic effects, that is, diffusion limitations, superimpose the latte- mentioned microscopic mechanisms and lead to a shrinkage of OCV hysteresis, if cells are loaded with high current rates. To validate the mentioned interaction, Li-ion cells' state of charge is adjusted to 50% with various current rates, beginning from the fully charged and the discharged state, respectively. As a pronounced difference remains between the OCV after charge and discharge adjustment, obviously the hysteresis vanishes as the target SOC is adjusted with very high current rate.

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OCV Hysteresis in Li-Ion Batteries including Two-Phase Transition Materials

Lithium-ion (Li-ion) batteries are nowadays the best tradeoff between performance, cost, and lifetime to store electric power and energy effectively. The reliable determination of the batteries' instantaneous power capability and energy content is mandatory for many mobile high-energy and power-consuming applications. Herein, the battery impedance and state of charge (SOC) are the relevant values. In energy storage systems, these values are to be considered for each battery cell individually, due to arising limitations caused by cell-specific variations. Simple algorithms to determine the battery system's impedance and SOC are presented including parameter and state-estimation techniques. Furthermore, methods are derived making the impedance and SOC determination possible for a large number of particular cells in a battery system. The applicability of the demonstrated algorithms for battery control unit implementation is proved incorporating data achieved from load scenarios applied to a cell stack Li-ion battery module. Cell impedance and SOC variations could be detected precisely. Moreover, a combination of impedance and SOC spread was identified during typical battery operation.

Michael Roscher

Papers

Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries

Current density and state of charge inhomogeneities in Li-ion battery cells with LiFePO4 as cathode material due to temperature gradients

Detection of Utilizable Capacity Deterioration in Battery Systems

OCV Hysteresis in Li-Ion Batteries including Two-Phase Transition Materials

Reliable State Estimation of Multicell Lithium-Ion Battery Systems