Distributed Intelligent Battery Management System

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Eduardo Quiles-Cucarella	--	3371	2023-12-14 12:01:49	\|
2	format correct	Catherine Yang	Meta information modification	3371	2023-12-15 01:43:44	\|

This entry is adapted from the peer-reviewed paper 10.3390/s23073417

For real-world applications, battery management systems (BMSs) can be used in the form of distributed control systems where general controllers, charge regulators, and smart monitors and sensors are integrated, such as those proposed in this work, which allow more precise estimations of a large set of important parameters, such as the state of charge (SOC), state of health (SOH), current, voltage, and temperature, seeking the safety and the extension of the useful life of energy storage systems based on battery banks.

battery management system temperature sensor charge regulator cloud computing ESS

1. Introduction

At present, a very worrying energy crisis is affecting socio-political, economic, and energy supply stability on a global scale. For this last aspect, it is particularly necessary to intensify the promotion of an energy self-sufficiency policy based on renewable energies.

One of the objectives for further increasing the competitiveness of renewable energies must be based on the improvement of energy storage systems (ESSs). In particular, it is important to improve the operating and maintenance costs of the installations based on the application of advanced methods for the monitoring and predictive diagnosis of failures, aimed at extending the useful life of the batteries. The price of a photovoltaic (PV) system is significantly impacted by the reduced battery life. More than 40% of the costs over the course of a PV system’s life cycle are attributable to the battery ^[1]. System reliability will improve, and operating costs will be significantly reduced as the battery’s lifespan increases ^[2]^[3]. By avoiding hazardous operating conditions such as deep discharge and overcharging, batteries’ lifespans can be increased.

A battery can offer many years of dependable service if it is properly designed, constructed, and maintained. A brand-new battery may not initially operate at full capacity. During the first few years of use, the capacity usually increases, peaks, and then drops until the battery reaches the end of its useful life. A lead–acid battery is generally considered to have reached the end of its useful life when its capacity reaches 80% of its rated capacity. Below 80%, the rate of battery deterioration quickens, making it more susceptible to a mechanical shock or a rapid failure induced by a high discharge rate. It should be noted that a battery will eventually lose its capacity even in perfect circumstances ^[4].

The storage capacity and useful life of batteries are significantly influenced by the appropriate management of their use, which is necessary to achieve the energy storage system’s maximum availability ^[5]. Batteries can be used differently by condition-monitoring devices to increase their lifespan. Additionally, they can regulate the state of the batteries, allowing the application of predictive maintenance approaches to decide when to replace them ^[6]^[7]. They can also estimate the quantity of energy stored in the batteries to plan power usage and charging cycles.

2. Maintenance Particularities of Each Type of Battery

The useful life of batteries depends on many factors, and it is known that it can be shortened by both overcharging and undercharging and overloading. It can also be highly detrimental if the current discharges are too deep under conditions of high ambient temperatures or due to the confined space of the installation site. This phenomenon is general and affects different types of batteries, either Li-ion type ^[8] or Valve-Regulated Lead–Acid (VRLA)-type batteries (gel or Absorbent Glass Mat (AGM)), which, due to their comparatively low cost, are still widely used ^[9].

For VRLA (gel or AGM) batteries, overcharging can cause gassing, which dries out the electrolyte and raises the internal resistance, leading to irreparable damage. When the charging voltage of 12 V batteries approaches 15 V, flat-plate VRLA batteries begin to leak water ^[1].

It should be noted that Li-ion batteries are particularly expensive and that they are also susceptible to irreversible damage from excessive charging or discharging. When the system is not in use, various components (alarm systems, relays, standby current from specific loads, current drain from battery chargers, etc.) gradually drain the battery. Since Li-ion batteries have a much higher charging efficiency than lead–acid batteries, it is advised to set the charging efficiency factor to 99 percent ^[10].

LiFePO4 batteries can generally use the factory-programmed “charging parameters” as well. When the current falls below a set threshold, some battery chargers stop charging. This limit must be exceeded by the tail current. LiFePO4 batteries outperform lead–acid batteries at high discharge rates. The Peukert exponent should be set to 1.05 ^[10] unless the battery manufacturer specifies otherwise.

When the system is not in use, the battery should be isolated in case there is any uncertainty regarding potential residual current consumption by opening its circuit breaker, removing the battery fuse(s), or disconnecting the battery’s positive terminal. If the system has been completely discharged and the low-voltage disconnection of the cells has taken place, the residual discharge current is particularly hazardous. A Li-ion battery still has a reserve of about 1 Ah for every 100 Ah of capacity after being disconnected due to the low cell voltage. If the battery’s reserve capacity is reduced, the battery will suffer damage. If the system is left in a discharged state for more than 10 days, a residual current of 4 mA, for instance, can destroy a 100 Ah battery (4 mA × 24 h × 10 days = 0.96 Ah). It is especially advised in these circumstances to use devices with low current consumption ^[10].

3. BMS Applications

By managing the battery with an intelligent battery management system (BMS), information is received that will allow major degradation problems to be avoided. The amortization of a BMS is quickly realized by contributing to the prolongation of battery life.

It would be desirable if a BMS could have easy access to the innards of commonly used batteries to protect the drive battery’s individual cells and to increase the service life as well as the cycle number. This possibility would be very interesting for most types of batteries. However, this is not feasible for practically all commercial batteries, except in the case of laboratory tests. In this final instance, the BMS measures the control parameters: the battery current, temperature, and cell voltage. The nominal voltage of a common battery cell is 3.6 V, with a maximum end-of-charge voltage of 4.2 V and a minimum end-of-discharge voltage of 2.5 V. Unrepairable damage, such as capacity loss and an increase in self-discharging, is brought on by high discharging (<2.5 V). Overvoltage (>4.2 V) can cause spontaneous self-ignition, which is dangerous. When temperatures and voltages are excessively high while charging, there is a significant risk of capacity loss. A typical battery has a lifespan of 500 to 1000 cycles when used properly before losing 20% of its initial capacity.

The forecasting of a battery’s state of charge (SOC) and state of health (SOH) is possible in part by monitoring the cell voltage, current, and temperature. SOC refers to the battery’s present level of charge in relation to its maximum capacity. SOH describes the battery’s present health in comparison to a brand-new battery ^[11].

In summary, among the main functions to be performed on batteries by a BMS are charge and discharge control ^[12], thermal management ^[13], battery equalization ^[14], fault diagnosis ^[15]^[16]^[17], data acquisition, communication ^[18], and the estimation of the battery’s state of charge ^[19]^[20], state of energy (SoE) ^[21], state of power (SoP) ^[22], and state of health (SoH) ^[23]. Especially in top-priority safety-critical cases, they require the application of safeguarding functions to the system, including battery disconnection operations from generation or consumption sources ^[24].

Recent research ^[25]^[26]^[27] has reported smart BMS systems to implement specific functions in critical applications. The aircraft industry, automotive sector ^[28], and renewable energy grid integration applications ^[26] are fields where the BMS plays an essential role in system performance. Aircraft electrification’s modern challenges ^[29] involve migrating from hydraulic or pneumatic onboard systems to electrical systems. This is the so-called “More electric Aircraft” (MEA) research field. European projects such as I-PRIMES ^[30], MOET ^[31], EPOCAl ^[32], and ENIGMA ^[27] tackle the control of the electrical system. The optimal operation of the batteries leads to efficient battery sizing, which involves less weight, a critical aspect in airplanes. Therefore, the design of more innovative MEA control systems ^[29] (including BMSs) is necessary. Electric vehicles are also a hot topic in BMS research ^[33]^[34]. As in the aircraft industry, optimal battery sizing reduces the car’s weight so that autonomy can be increased with optimal BMS development.

4. Battery Modeling

Numerous researchers ^[35] have investigated the dynamic behavior of battery operation. Lead–acid batteries were used to power the models created years ago ^[36]^[37]^[38], but lithium and nickel–cadmium batteries are similar in some ways. Lead–acid chemistry is similar to nickel–cadmium (NiCd) chemistry in that an electrolyte contains two different metals. Unlike sulfuric acid in lead–acid batteries, potassium hydroxide (KOH) does not enter the reaction in NiCd batteries. Because the positive and negative plates alternate while being submerged in an electrolyte, the manufacturing process is comparable to lead–acid batteries ^[39]. A summary of the mathematical lithium and NiCd battery models created at the University of South Carolina is provided in ^[40]. Numerous authors have developed in-depth dynamic models of lithium batteries. These range from straightforward models with resistance (R) or parallel-resistance capacitors (RC) ^[41]^[42]^[43] to more intricate models with phase change components and coils ^[44]^[45]. Placing these components in series and incorporating particularities to achieve higher levels of adjustment in the electrical behavior of the battery was the researchers’ primary method of operation ^[46]^[47].

Other mathematical models have been developed in addition to these electrical models to estimate the parametric variations depending on the values associated with the time of use/disuse and changes in battery temperature ^[48]. The remainder of the battery’s useful life (RUL) is calculated using these models after the effects of aging have been added. The temperature (T), depth of discharge (DOD), state of charge (SOC), and discharge velocity (C-rate) are the primary variables that influence battery aging mechanisms ^[49]^[50]^[51].

Analyzing the aging phenomenon is necessary to keep track of a battery’s useful life. Batteries experience both calendar aging, which occurs when they are stationary, and cycling aging, which occurs when they undergo cyclic operation ^[35]. The temperature and the SOC are two primary factors that contribute to aging in the first scenario, in addition to time itself. The Arrhenius equation can be used to explain how the temperature changes exponentially ^[52], whereas the SOC changes linearly ^[53]^[54]. On the other hand, the DOD and the C-rate also play a role in aging brought on by cycling ^[55]. In contrast to the C-rate, which uses a second-degree polynomial, the first one accomplishes this using a logarithmic relation ^[56]^[57].

Both a rise in the battery’s internal resistance and a reduction in capacity are the two consequences that these effects have in practice ^[58]. The RC components are also affected, but these effects only have an immediate impact on how quickly the battery reacts to sudden changes in current. Practically speaking, a relationship can be drawn between the battery’s capacity loss and aging, which will continue to happen continuously despite taking place slowly.

The gradual loss of a battery’s capacity and its increase in internal resistance are the most obvious effects of battery aging, and these are measured by the state of the battery (SOH) parameter. The SOH can be calculated either using Equation (1) or (2) as the ratio of the battery’s current capacity (Cap) to its initial capacity (Cap_ini) or its initial internal resistance Rn_ini to increased internal resistance Rinc, respectively.

SOH = Cap/Cap_ini

(1)

SOH = Rinc/Rn_ini

(2)

Its operational limit will be established by the SOH. In other words, this is the parameter that determines when a battery in a given application reaches the end of its useful life. The aging of batteries and other relevant parameters have been analyzed through experimental tests performed in laboratories following predetermined protocols ^[59]^[60]^[61]^[62]^[63].

When taking into account a battery’s actual operating circumstances, sensors may frequently be unable to determine or estimate aging parameters by measuring their internal variables. Mathematical models developed in experimental laboratory conditions do not easily adapt to the random conditions of complex degradation associated with the real-world operating regime that batteries are subjected to in challenging and difficult circumstances because measurements can be very challenging, expensive, or intractable. Batteries gradually degrade under these circumstances as a result of multi-parametric cumulative effects, the relative contributions of which are very difficult to measure. Online trend analysis techniques are more suitable for monitoring the true state of charge (SOH) of batteries when operating under actual conditions ^[64].

5. Common Catastrophic Battery Failures

Finding out when batteries’ useful lives are up with the potential to move on to adequate maintenance in the appropriate time and form is one of the key components of diagnosis. However, it is also important to take into account the risks related to some common catastrophic failures, whose effects might be felt immediately. According to their technology, batteries are known to fail in a variety of ways ^[65]. Due to their relative advantages, relatively low cost of installation and maintenance, energy density, and safety, some of the most popular battery types today are the VRLA and gel types. The associated typical failure modes for these types of batteries are those that are listed below ^[66]^[67]:

Dry-out (loss-of-compression);
Plate sulfation;
Soft and hard shorts;
Post leakage;
Thermal runaway;
Positive-grid corrosion.

Many of the above-mentioned failure modes, particularly dry-out, positive-grid corrosion, and thermal runaway, are strongly influenced by an increase in the internal battery temperature, which in turn depends, under normal circumstances, primarily but not exclusively on the ambient temperature resulting from environmental factors such as weather. The battery installation chamber acts as a “filter” to change the outside temperature to the ambient temperature.

The internal battery temperature has a significant impact on aging, grid corrosion, and the rate of water loss (dry-out) from evaporation or hydrogen evolution at the negative plates (self-discharge), all of which rise with temperature. On the other hand, applications that involve intense cycling may benefit from a (moderate) temperature increase ^[68].

The phenomenon of dry-out occurs and is accelerated by excessive heat (lack of proper ventilation or, to put it another way, heat accumulation inside the battery as a result of a prior failure from the heat dissipation process), as well as overcharging, which can result in elevated internal temperatures and high ambient (installation chamber) temperatures, and significantly contributes to grid corrosion. Failures can show signs of drying out in between 82 and 85% of cases ^[67]. It frequently occurs as a side effect of some failure modes and as a unique inducer of others, such as thermal runaway. Negative-strap corrosion, which causes the slow loss of the electrolyte, is the typical failure mode for a VRLA battery under normal operating conditions. The pressure relief valve (PRV) allows the sealed cells to vent when the internal temperature rises. The battery capacity decreases, and the internal impedance rises when enough electrolyte is vented, removing the glass matte from contact with the plates.

When a battery experiences thermal runaway, its temperature rises quickly, causing it to overheat to an extreme degree. As a result, the battery may melt, catch fire, or even explode. Only high ambient temperatures and/or excessive charging voltage can cause thermal runaway in a battery ^[69]^[70]. Even though runaway failures are less common, they can still have critical consequences. In these situations, the battery system’s automatic control action must be immediate and based on its quick isolation from the loads and disconnection from the generation sources, both of which must be carried out by the BMS system as a whole. In a self-sustaining reaction, thermal runaway happens when a battery’s internal parts start to melt. The battery’s internal temperature increases as the current is accepted. The battery can accept more current from the charger because the temperature increase lowers the impedance of the battery. The battery gets hotter as a result of the higher current. When the heat removed linearly increases more slowly than the heat produced by the reaction, thermal runaway occurs. As a result of the reaction mass’s temperature being raised by the excess heat, the rate of reaction rises. In turn, this quickens the rate at which heat is produced. According to a rough rule of thumb, the reaction rate—and subsequently, the rate of heat generation—doubles with every 10 C increase in temperature, causing the battery temperature to “runaway”. The temperature can rise even further to the point of plastic meltdown and potential fire ^[66] once the electrolyte has boiled away, exceeding the upper limit of 126 °C ^[67] that will eventually be reached when it begins to boil.

The float voltage and ambient temperature can have varying effects on various batteries. Significant variations in the float voltages between various batteries of various makes and models can result in varying aging times. The chemistry of the battery and its construction, its age, and particularly the chamber conditions where batteries are installed all play a part in the response variation in each battery’s behavior ^[71].

If the battery’s internal heat generation process enters an advanced uncontrolled phase, violent boiling and quick gas generation will take place, leading to over-pressurization. If this condition is not caught in time, it can result in catastrophic damage due to emissions of hydrogen, oxygen, hydrogen sulfide gas (an irritant), and atomized electrolyte. The installation chamber may catch fire or explode as a result of this process ^[72]. Users may be exposed to hazardous gas emissions in this situation, including hot, toxic gases, liquids, and particles, increasing the possibility of catastrophic mishaps.

All batteries are known to be “killed” by high temperatures, though the impact varies depending on the model, manufacturer, and manufacturing technology used. At 95 °F (35 °C), the life of lead–acid is reduced by 50%, whereas the life of nickel–cadmium is reduced by 16–18% ^[56]^[62]. The battery life is halved for every 18 °F (10 °C) increase in battery temperature. Positive-grid corrosion occurs more quickly as a result of the elevated temperature, as do other failure modes. A 20-year battery will only last 10 years if it is kept at a temperature of 95 °F (35 °C) as opposed to the intended 77 °F (25 °C), and so on. A 20-year battery will only last 5 years if the temperature is raised by another 18 °F to 113 °F (45 °C). The internal temperature of the batteries is therefore the most crucial parameter to take into account in predictive trend analysis ^[56].

On the other hand, any battery’s internal chemical reactions are slowed down by the low-temperature range. Depending on the technology, the degree of performance reduction varies as well. A VRLA battery might need a 20% capacity compensation, for instance, when the temperature is close to freezing. A capacity increase of twofold is necessary for a lead–calcium cell using 1.215 specific gravity acid, compared to an 18% increase for the Ni-Cd cell.

In a perfect world, the trend analysis of some battery parameters, especially temperature, impedance, capacity, and their relationship with SOH, would be a great tool for determining when it is time to replace the batteries and how the batteries degrade over time. However, as was already mentioned, not all of these parameters can be easily assessed ^[73]^[74]^[75].

However, despite the unquestionable advancements brought about by the introduction of BMSs, there are still some issues that limit the ability to fully utilize the potential for performing estimations with more precise models referring to the internal state parameters of the batteries, such as SOC and SOH. The limitations on computing power and data storage that are currently in place make this problem more difficult. The most recent proposals that have been made in recent years, based on the use of IoT, cloud computing, twin models, big data, and machine learning, aim to eliminate the current difficulties ^[76]^[77]^[78]^[79].

In this work, as a contribution, a decentralized but synchronized real-world smart battery management system has been designed using a Cerbo GX general controller with networking communication capability and cloud data processing access, four charge regulators, and a sensorized smart battery monitor with networking and Bluetooth capabilities. Currently, BMSs can be utilized as distributed control systems for real-world applications when general controllers, charge regulators, and smart monitors and sensors are integrated, such as those suggested in this work, which enable more accurate estimations of the battery’s electrical parameters.

The main feature of the proposed BMS is that it is intelligent, as it provides the ability to supply dynamic parameters from a non-intelligent battery in a similar way to intelligent batteries. It is a real-world BMS system and a comparatively low-cost system.

References

Bopp, G.; Gabler, H.; Sauer, D.; Jossen, A.; Mittermeier, J.; Bachler, M.; Sprau, P.; Willer, B.; Wollny, M. A Systematic Effort to Define Evaluation and Performance Parameters and Criteria for Lead Acid batteries in PV Systems. In Proceedings of the 13th European Photovoltaic Solar Energy Conference, Nice, France, 23–27 October 1995; pp. 1763–1769.
Markvart, T. Solar Electricity England; John Wiley & Sons: Hoboken, NJ, USA, 1994.
Duryea, S.; Islam, S.; Lawrance, W. A battery management system for stand alone photovoltaic energy systems. In Proceedings of the Conference Record of the 1999 IEEE Industry Applications Conference, Phoenix, Arizona, USA, 3–7 October 1999; Thirty-Forth IAS Annual Meeting (Cat. No. 99CH36370). Volume 4, pp. 2649–2654.
Lead Acid Battery Working–Lifetime Study. Available online: http://www.power-thru.com/documents/The%20Truth%20About%20Batteries%20-%20POWERTHRU%20White%20Paper.pdf (accessed on 12 July 2019).
Lee, C.Y.; Peng, H.C.; Lee, S.J.; Hung, I.; Hsieh, C.T.; Chiou, C.S.; Chang, Y.-M.; Huang, Y.P. A flexible three-in-one microsensor for real-time monitoring of internal temperature, voltage and current of lithium batteries. Sensors 2015, 15, 11485–11498.
Hong, J.; Wang, Z.; Liu, P. Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles. Energies 2017, 10, 919.
Jouhara, H.; Khordehgah, N.; Serey, N.; Almahmoud, S.; Lester, S.P.; Machen, D.; Wrobel, L. Applications and thermal management of rechargeable batteries for industrial applications. Energy 2019, 170, 849–861.
Sidhu, A.; Izadian, A.; Anwar, S. Adaptive nonlinear model-based fault diagnosis of Li-ion batteries. IEEE Trans. Ind. Electron. 2014, 62, 1002–1011.
García, E.; Quiles, E.; Correcher, A.; Morant, F. Marine NMEA 2000 Smart Sensors for Ship Batteries Supervision and Predictive Fault Diagnosis. Sensors 2019, 19, 4480.
Available online: https://www.victronenergy.com/upload/documents/BMV-712_Smart/9172-Manual_BMV_and_SmartShunt-pdf-en.pdf (accessed on 15 March 2023).
Dorn, R.; Schwartz, R.; Steurich, B. Battery Management System. In Lithium-Ion Batteries: Basics and Applications; Springer: Berlin, Heidelberg, 2018; pp. 165–175.
Jiang, Z.; Jia, Q.; Guan, X. On large action space in EV charging scheduling optimization. Sci. China Inform. Sci. 2022, 65, 122201.
Liu, H.; Wei, Z.; He, W. Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: A review. Energy Convers. Manag. 2017, 150, 304–330.
Wang, Y.; Zhang, C.; Chen, Z.; Xie, J.; Zhang, X. A novel active equalization method for lithium-ion batteries in electric vehicles. Appl. Energy 2015, 145, 36–42.
Jia, S.; Ma, B.; Guo, W.; Li, Z.S. A sample entropy based prognostics method for lithium- ion batteries using relevance vector machine. J. Manuf. Syst. 2021, 11, 2169–2189.
Tran, M.-K.; Fowler, M. Sensor Fault Detection and Isolation for Degrading Lithium-Ion Batteries in Electric Vehicles Using Parameter Estimation with Recursive Least Squares. Batteries 2020, 6, 1.
Hu, X.; Zhang, K.; Liu, K.; Lin, X.; Dey, S.; Onori, S. Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures. IEEE Ind. Electron. Mag. 2020, 14, 65–91.
Wang, Y.; Tian, J.; Chen, Z.; Liu, X. Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles. Measurement 2019, 131, 443–451.
Wang, Y.; Chen, Z. A framework for state-of-charge and remaining discharge time prediction using unscented particle filter. Appl. Energy 2020, 260, 114324.
Wang, Y.; Zhang, C.; Chen, Z. A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries. Appl. Energy 2014, 135, 81–87.
He, H.; Zhang, Y.; Xiong, R.; Wang, C. A novel Gaussian model based battery state estimation approach: State-of-energy. Appl. Energy 2015, 151, 41–48.
Wang, Y.; Pan, R.; Liu, C.; Chen, Z.; Ling, Q. Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation. J. Power Sources 2018, 374, 12–23.
Dong, G.; Han, W.; Wang, Y. Dynamic Bayesian network based lithium-ion battery health prognosis for electric vehicles. IEEE Trans. Ind. Electron. 2020, 68, 10949–10958.
Wang, Y.; Xu, R.; Zhou, C.; Kang, X.; Chen, Z. Digital twin and cloud-side-end collaboration for intelligent battery management system. J. Manuf. Syst. 2022, 62, 124–134.
Gabbar, H.A.; Othman, A.M.; Abdussami, M.R. Review of Battery Management Systems (BMS) Development and Industrial Standards. Technologies 2021, 9, 28.
Arnieri, E.; Boccia, L.; Amoroso, F.; Amendola, G.; Cappuccino, G. Improved Efficiency Management Strategy for Battery-Based Energy Storage Systems. Electronics 2019, 8, 1459.
Sumsurooah, S.; He, Y.; Torchio, M.; Kouramas, K.; Guida, B.; Cuomo, F.; Atkin, J.; Bozhko, S.; Renzetti, A.; Russo, A.; et al. ENIGMA—A Centralised Supervisory Controller for Enhanced Onboard Electrical Energy Management with Model in the Loop Demonstration. Energies 2021, 14, 5518.
Aiello, O. Electromagnetic Susceptibility of Battery Management Systems’ ICs for Electric Vehicles: Experimental Study. Electronics 2020, 9, 510.
PRIMES: An Intelligent Power Regulation Using Innovative Modules for Energy Supervision. Available online: https://cordis.europa.eu/article/id/173624-smart-control-of-aircraft-electrical-loads (accessed on 11 March 2023).
More Open Electrical Technologies (MOET). Available online: https://cordis.europa.eu/project/id/30861 (accessed on 11 March 2023).
EPOCAL: An Electrical POwer Center for Aeronautical Loads. Available online: https://cordis.europa.eu/project/id/323408 (accessed on 11 March 2023).
Canciello, G.; Cavallo, A.; Schiavo, A.L.; Russo, A. Multi-objective adaptive sliding manifold control for More Electric Aircraft. ISA Trans. 2020, 107, 316–328, ISSN 0019-0578.
Ramkumar, M.S.; Reddy, C.S.R.; Ramakrishnan, A.; Raja, K.; Pushpa, S.; Jose, S.; Jayakumar, M. Review on Li-Ion Battery with Battery Management System in Electrical Vehicle. Adv. Mater. Sci.Eng. 2022, 2022, 3379574.
Lipu, M.S.H.; Hannan, M.A.; Karim, T.F.; Hussain, A.; Saad, M.H.M.; Ayob, A.; Miah, M.S.; Indra Mahlia, T.M. Intelligent algorithms and control strategies for battery management system in electric vehicles: Progress, challenges and future outlook. J. Clean.Prod. 2021, 292, 126044, ISSN 0959-6526.
Casals, L.C.; García, B.A.; Benítez, M.M.G. Aging model for re-used electric vehicle batteries in second life stationary applications. In Project Management and Engineering Research; Springer: Cham, Switzerland, 2017; pp. 139–151.
Salameh, Z.M.; Casacca, M.A.; Lynch, W.A. A mathematical model for lead-acid batteries. IEEE Trans. Energy Convers. 1992, 7, 93–98.
Copetti, J.B.; Lorenzo, E.; Chenlo, F. A general battery model for PV system simulation. Prog. Photovolt. Res. Appl. 1993, 1, 283–292.
Ceraolo, M. New dynamical models of lead-acid batteries. IEEE Trans. Power Syst. 2000, 15, 1184–1190.
Chen, M.; Rincon-Mora, G.A. Accurate electrical battery model capable of predicting runtime and IV performance. IEEE Trans. Energy Convers. 2006, 21, 504–511.
Gomadam, P.M.; Weidner, J.W.; Dougal, R.A.; White, R.E. Mathematical modeling of lithium-ion and nickel battery systems. J. Power Sources 2002, 110, 267–284.
Zhang, J.; Lee, J. A review on prognostics and health monitoring of Li-ion battery. J. Power Sources 2011, 196, 6007–6014.
Cho, S.; Jeong, H.; Han, C.; Jin, S.; Lim, J.H.; Oh, J. State-of-charge estimation for lithium-ion batteries under various operating conditions using an equivalent circuit model. Comput. Chem. Eng. 2012, 41, 1–9.
Xu, J.; Wang, J.; Li, S.; Cao, B. A method to simultaneously detect the current sensor fault and estimate the state of energy for batteries in electric vehicles. Sensors 2016, 16, 1328.
Osaka, T.; Momma, T.; Mukoyama, D.; Nara, H. Proposal of novel equivalent circuit for electrochemical impedance analysis of commercially available lithium ion battery. J. Power Sources 2012, 205, 483–486.
Liu, W.; Delacourt, C.; Forgez, C.; Pelissier, S. Study of graphite/NCA Li-ion Cell Degradation During Accelerated Aging Tests—Data Analysis of the SimStock Project. In Proceedings of the 2011 IEEE Vehicle Power and Propulsion Conference, Chicago, IL, USA, 6–9 September 2011; pp. 1–6.
Guenther, C.; Barillas, J.K.; Stumpp, S.; Danzer, M.A. Adynamic battery model for simulation of battery-to-grid applications. In Proceedings of the 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin, Germany, 14–17 October 2012; pp. 1–7.
Worwood, D.; Kellner, Q.; Wojtala, M.; Widanage, W.D.; McGlen, R.; Greenwood, D.; Marco, J. A new approach to the internal thermal management of cylindrical battery cells for automotive applications. J. Power Sources 2017, 346, 151–166.
Lai, T.C.; Tsang, K.F.; Liu, Y.; Lai, L.L. A Temperature Prediction Method of Valve-regulated Lead-acid Battery. In Proceedings of the 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada, 12–14 June 2019; pp. 2467–2470.
Barré, A.; Deguilhem, B.; Grolleau, S.; Gérard, M.; Suard, F.; Riu, D. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J. Power Sources 2013, 241, 680–689.
Eddahech, A. Modelisation duVieillissement et Determination de l’Etat de Sante de Batteries Lithium-Ion pour Application Vehicule Electrique et Hybride. Available online: https://tel.archives-ouvertes.fr/tel-00957678 (accessed on 12 July 2019).
Vetter, J.; Nov, P.; Wagner, M.R.R.; Veit, C.; Novák, P.; Möller, K.-C.; Hammouche, A. Ageing mechanisms in lithium-ion batteries. J. Power Sources 2005, 147, 269–281.
Laidler, K.J. The development of the Arrhenius equation. J. Chem. Educ. 1984, 61, 494.
Schmalstieg, J.; Käbitz, S.; Ecker, M.; Sauer, D.U. A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries. J. Power Sources 2014, 257, 325–334.
Delaille, A.; Grolleau, S.; Duclaud, F. SIMCAL Project: Calendar aging results obtained on a panel of 6 commercial Li-ion cells. In Proceedings of the Electrochemical Energy Summit de l’Electrochemical Society, San Francisco, CA, USA, 27–28 October 2013; Volume 101.
Guena, T.; Leblanc, P. How Depth of Discharge A ects the Cycle Life of Lithium-Metal-Polymer Batteries. In Proceedings of the INTELEC 06—Twenty-Eighth International Telecommunications Energy Conference, Providence, RI, USA, 10–14 September 2006; pp. 1–8.
Sarasketa-Zabala, E.; Laresgoiti, I.; Alava, I.; Rivas, M.; Villarreal, I.; Blanco, F. Validation of the methodology for lithium-ion batteries lifetime prognosis. In Proceedings of the EVS27 Electric Vehicle Symposium 2013, Barcelona, Spain, 17–20 November 2013; pp. 1–12.
Lam, L. A Practical Circuit based Model for State of Health Estimation of Li ion Battery Cells in Electric Vehicles. Master’s Thesis, University of Technology Delft, Delft, Switzerland, 2011.
Nieho, P.; Kraemer, E.; Winter, M. Parametrisation of the influence of di erent cycling conditions on the capacity fade and the internal resistance increase for lithium nickel manganese cobalt oxide/graphite cells. J. Electroanal. Chem. 2013, 707, 110–116.
Goebel, K.; Saha, B.; Saxena, A.; Celaya, J.R.; Christophersen, J.P. Prognostics in battery health management. IEEE Instrum. Meas. Mag. 2008, 11, 33–40.
Nuhic, A.; Terzimehic, T.; Soczka-Guth, T.; Buchholz, M.; Dietmayer, K. Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 2013, 239, 680–688.
Zou, Y.; Hu, X.; Ma, H.; Li, S.E. Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles. J. Power Sources 2015, 273, 793–803.
Dai, H.; Wei, X.; Sun, Z. A new SOH prediction concept for the power lithium-ion battery used on HEVs. In Proceedings of the 5th IEEE Vehicle Power and Propulsion Conference, VPPC 09, Dearborn, MI, USA, 7–10 September 2009; pp. 1649–1653.
Zainuri, A.; Wibawa, U.; Rusli, M.; Hasanah, R.N.; Harahap, R.A. VRLA battery state of health estimation based on charging time. Telkomnika 2019, 17, 1577–1583.
NASA-STD-8070.5; Trend Analysis Technique. NASA: Washington, DC, USA, 1988.
May, G.J.; Davidson, A.; Monahov, B. Lead batteries for utility energy storage: A review. J. Energy Storage 2018, 15, 145–157.
Megger Batery Testing Guide. art.nr. ZP-AD01E Doc. AD0009AE 2009. Available online: https://us.megger.com/support/technical-library?searchtext=&searchmode=anyword&application2=0&type=6;&application=0&order=0 (accessed on 10 October 2019).
McCluer, S. Battery Technology for Data Centers and Network Rooms: Lead-Acid Battery Options; APC White Paper; Schneider Electric: Rueil-Malmaison, France, 2011; Volume 30.
Technical Manual for Chairman Series Batteries; Concorde Battery Corporation: West Covina, CA, USA, 2017.
Catherino, H.A. Complexity in battery systems: Thermal runaway in VRLA batteries. J. Power Sources 2006, 158, 977–986.
Culpin, B. Thermal runaway in valve-regulated lead-acid cells and the e ect of separator structure. J. Power Sources 2004, 133, 79–86.
Ho, C.M.; Steves, K. Newinsights into thermal runaway of valve regulated lead-acid batteries. In Proceedings of the International Stationary Battery Conference, Battcon, Miami Beach, FL, USA; Available online: https://www.sbsbattery.com/PDFs/VRLAThermRunawayStorageBatterySystems.pdf (accessed on 10 October 2019).
McCluer, S. Battery Technology for Data Centers and Network Rooms: VRLA Reliability and Safety; Schneider Electric: Rueil-Malmaison, France, 2012.
Feder, D.O.; Hlavac, M.J. Analysis and interpretation of conductance measurements used to assess the state-of-health of valve regulated lead acid batteries. In Proceedings of the Intelec 94, Vancouver, BC, Canada, 30 October–3 November 1994; pp. 282–291.
Cotton, C.B.; Sheppard, D.; Lim, R.; Cotton, S.D.; Jump, M. System and Method for Remote Monitoring of Battery Condition. U.S. Patent 7,768,238, 18 August 2009.
Deshpande, S.; Shaffer, D.; Szymborski, J.; Barling, L.; Hawkins, J. Intelligent monitoring system satisfies customer needs for continuous monitoring and assurance on VRLA batteries. In Proceedings of the 21st International Telecommunications Energy Conference. INTELEC’99, Copenhagen, Denmark, 9 June 1999; p. 557.
Kim, T.; Makwana, D.; Adhikaree, A.; Vagdoda, J.; Lee, Y. Cloud-Based Battery Condition Monitoring and Fault Diagnosis Platform for Large-Scale Lithium-Ion Battery Energy Storage Systems. Energies 2018, 11, 125.
Li, W.; Rentemeister, M.; Badeda, J.; Jöst, D.; Schulte, D.; Sauer, D.U. Digital twin for battery systems: Cloud battery management system with online state-of-charge and state-of-health estimation. J. Energy Storage 2020, 30, 101557.
Yang, S.; Zhang, Z.; Cao, R.; Wang, M.; Cheng, H.; Zhang, L.; Jiang, Y.; Li, Y.; Chen, B.; Ling, H.; et al. Implementation for a cloud battery management system based on the CHAIN framework. Energy AI 2021, 5, 100088.
Tran, M.K.; Panchal, S.; Khang, T.D.; Panchal, K.; Fraser, R.; Fowler, M. Concept review of a cloud-based smart battery management system for lithium-ion batteries: Feasibility, logistics, and functionality. Batteries 2022, 8, 19.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Automation & Control Systems

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Emilio García

Eduardo Quiles

Antonio Correcher

View Times: 90

Update Date: 15 Dec 2023

Table of Contents

Video Upload Options

Confirm