Oshnoei, Arman

Sorouri, Hoda

Teodorescu, Remus

English

    Aalborg UniversitetIntelligent Cell Balancing Control for Lithium-Ion Battery PacksSorouri, Hoda; Oshnoei, Arman; Teodorescu, RemusPublished in:IPEMC 2024-ECCE Asia - 10th International Power Electronics and Motion Control Conference - ECCE AsiaPublication date:2024Link to publication from Aalborg UniversityCitation for published version (APA):Sorouri, H., Oshnoei, A., & Teodorescu, R. (2024). Intelligent Cell Balancing Control for Lithium-Ion BatteryPacks. In IPEMC 2024-ECCE Asia - 10th International Power Electronics and Motion Control Conference -ECCE AsiaGeneral rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            - Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            - You may not further distribute the material or use it for any profit-making activity or commercial gain            - You may freely distribute the URL identifying the publication in the public portal -Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: May 29, 2024Intelligent Cell Balancing Control for Lithium-IonBattery Packs1st Hoda SorouriDepartment of EnergyAalborg UniversityAalborg, Denmarkhoso@energy.aau.dk2nd Arman OshnoeiDepartment of EnergyAalborg UniversityAalborg, Denmarkaros@energy.aau.dk3rd Remus TeodorescuDepartment of EnergyAalborg UniversityAalborg, Denmarkret@energy.aau.dkAbstract—This study introduces a balancing control strategythat employs an Artificial Neural Network (ANN) to ensure Stateof Charge (SOC) balance across lithium-ion (Li-ion) batterypacks, consistent with the framework of smart battery packs.The model targets a battery pack consisting of cells with diversecharacteristics, reflecting real-world heterogeneous conditions. Afundamental aspect of this approach is the ability to bypassindividual cells optimally. This key feature stops current flowto and from the cell, allowing it to rest and cool off whileavoiding charging or discharging cycles. The implementation ofANN enables adaptive and dynamic management of SOC, whichis essential for optimizing performance and extending the lifespanof battery packs. The results demonstrate the effectiveness ofthe proposed ANN-based balancing strategy in SOC balancing,demonstrating its potential as a critical solution in enhancingbattery management systems for electric vehicles.Index Terms—The authors shall provide up to 4 keywords orphrases (in alphabetical order and separated by commas) to helpidentify the major topics of the paper.I. INTRODUCTIONThe continuous growth in electric vehicles is increasingLi-ion battery deployment. The production process of thesebatteries, including mixing, coating, calendaring, slitting, andformation [1], often faces challenges in achieving uniformityeven within the same production line [2] [3]. This incon-sistency may lead to overcharging or over-discharging [4],[5] due to the shortboard effect [6], thereby causing energyand capacity losses. To counter these imbalances, balancing isessential for maintaining cell consistency and efficiency [7].These batteries are controlled by a Battery ManagementSystem (BMS). The BMS’s critical role is to manage batteryusage within the pack equitably and protect against unfavor-able events, notably thermal runaway. A vital function of astandard BMS is to ensure a balanced State of Charge (SOC)across cells. This is often achieved by incorporating activecooling systems to minimize thermal runaway risks.However, conventional SOC balancing techniques in BMSencounter various challenges, which are mainly divided intodissipative and non-dissipative balancing [8]– [9]. Dissipativebalancing, while straightforward, converts surplus energy fromhigher-energy cells into heat through resistors [10], [11], thusraising efficiency concerns and thermal management risks [8],[12], [13]. Its limitations in battery utilization [14] have ledto the exploration of non-dissipative balancing methods [15]-[19]. Non-dissipative balancing strategies aim to redistributecharge within the battery pack without significant encounterchallenges.There are different types of non-dissipative balancing.Capacitor-based systems [15], which, although efficient inbalancing, require a large number of components, increasingcosts and adding to the system’s complexity. Inductor-basedstrategies [16] offer a higher power handling capability butneed to be improved by their intricate control requirementsand higher costs. Transformer-based balancing systems [17],noted for their robustness, are often less efficient and bulkier,necessitating complex control systems and incurring highercosts. Common converter-based balancing [18], a compre-hensive solution in theory, faces practical challenges such asincreased size, complexity, and cost [19]. These constraintsof both dissipative and non-dissipative methods underscorethe need for a more efficient and simpler solution for SOCbalancing in BMS.This study emphasizes the importance of a low-cost andstraightforward control mechanism for SOC balancing [20],utilizing power switches for active cell balancing. The chosenbalancing approach modifies the battery pack’s current flowthrough switches, accommodating cells under different con-ditions. Its success hinges on the strategic design of switchoperations to ensure comprehensive cell balance, thereby by-passing the requirement for passive elements like capacitors,inductors, or converters. These advantages inform its choicefor this study, with switch control posing the main challenge.This paper introduces an innovative method that integratesArtificial Neural Networks (ANN) within a smart battery packframework. In response to these challenges, this paper pro-poses an innovative method employing ANN within the frame-work of the smart battery pack. This method is remarkablefor its dynamic management of the SOC in series-connectedlithium-ion battery cells, facilitated by the precise control ofhalf-bridge switches associated with each cell. A key featureof the proposed approach is the ability to bypass individualcells selectively, thus preventing unnecessary current flow andpromoting more efficient SOC balancing. The methodologybegins with creating an accurate model of a laboratory lithium-ion battery for real-world behavior simulation. Followingthis, an ANN is developed and implemented to manage theFig. 1. Electrical schematic of the proposed intelligent balancing system for a battery pack consisting of n serially connected cells.Fig. 2. Schematic of the proposed ANN-based balancing control scheme.SOC dynamically and accurately, substantially improving thebattery pack’s overall performance. The findings validate theeffectiveness of the suggested intelligent balancing controlapproach.II. PROPOSED CONTROL SYSTEMEvery cell is arranged in a series connection within anintelligent battery pack configuration. It incorporates a half-bridge design consisting of two complementary switches,denoted as Si and S′i, as illustrated in Figure 1. This designallows for the selective bypassing of individual cells. When thebottom switch, S′i, is activated, it results in bypassing a cell,particularly useful when a cell’s SOC is low to prevent rapiddischarge. This bypass function is pivotal for equalizing thehealth and performance of all cells in the pack. Additionally,this method leads to a pulsed current flow through the cells.Previous studies have shown that pulsed charging can enhancecell longevity. The smart battery pack’s architecture in thismodel is applied not only to improve the cycle life of Li-ion battery packs but also to achieve effective SoC balancingamidst the variations in individual cell characteristics. Theindividual cells are modeled as circuits, each comprising twoResistor-Capacitor (RC) pairs in series with internal resistance,as depicted in Fig. 1. This model is grounded in an electricalcircuit model used for Li-ion cells. The key parameters ofthe model are extracted from [21]. These specifications werederived from the Electrochemical Impedance Spectroscopy(EIS) test.The primary goal of the proposed framework is to minimizeSOC variations from the average value. ANN generate precisecontrol signals to create PWM signals for switch operation,thus ensuring balanced control.ANNs utilize processing units known as neurons, notable fortheir nonlinear nature. Each neuron consists of weights wij =[w1j , w2j , . . . , wnj ], an activation function g(j), and a bias φ.The choice of activation function varies, including options likethe sign, tangent sigmoid, and logarithmic sigmoid. The inputsyi are processed with these weights, and the output from thehidden layer Hj is derived from the weighted inputs and biasas demonstrated in Equation (1):Hj = g(n∑i=1wijyi + φ)for j = 1, 2, . . . , L (1)Here, L illustrates the total number of nodes in the hiddenlayer.The outputs are computed as:Oc =L∑j=1Hjwjc + φ for c = 1, 2, . . . ,m (2)with m denoting the node numbering in the output layer.The overall functionality of the SOC balancing implemen-tation is shown in a concept diagram in Fig. 1. At first, theaverage SOC should be calculated as follows:SOCAV =1nn∑i=1SOCi ∀i ∈ n (3)where n represents the number of cells; and SOCi indicatesthe SOC of each individual cell. The SOCs of the cellsare maintained within a standard operating range of 10-90%to prevent overcharging and overdischarging. This approachspecifically avoids the SOC extremes of 0-10% and 90-100%,where aging of the cells tends to accelerate. An ANN-basedcontroller is proposed to generate the control commands foreach cell. The ANN takes as input the variance in SOC valuesof each cell relative to SOCAV, as well as their respective ratesof change. It processes these inputs to intelligently generateduty cycle signals for pulse width modulation (PWM). ThePWM then generates the switching signals for the half-bridgeswitches associated with each cell, facilitating precise andadaptive SOC balancing. The schematic of the proposed ANN-based balancing control scheme is shown in Fig. 2.Various functions, such as sign, logsigmoid, and tansigmoid,can serve as activation functions. In this case, the sign functionis adopted as per the methodology in [22]. The objective of thelearning process is to reduce the mean squared error (MSE),which is calculated as followsMSE =1NN∑t=1n∑i=1(SOCi − SOCAV )2 (4)In this model, N denotes the aggregate count of samples.The initial layer of the neural network comprises 12 linearneurons, while the hidden layers are equipped with 50 neuronsthat exhibit nonlinear characteristics. These nonlinear elementsensure a gradual modification of the weights within the net-work during operations. The final layer of the neural networkis designed with ’n’ linear neurons, each representing anindividual cell. A back-propagation method under supervisedlearning conditions is applied to refine the learning process.wij(k + 1) = wij(k) + ηδyfor i = 1, 2, . . . , n; j = 1, 2, . . . , L(5)wjc(k + 1) = wjc(k) + ηHjEcfor j = 1, 2, . . . , L; c = 1, 2, . . . ,m(6)It should be observed in Figure 2 that wij indicates the weightvector associated with the hidden layer, while wjc is utilizedcomparably for the output layer. The term Ec representsthe error signals, including variations in the SOC and itsderivative.III. RESULTS AND DISCUSSIONSSimulations were conducted on a battery pack comprisingsix distinct cells, each varying slightly in terms of capacitiesand 2RC values. A 3.7V/50Ah NMC LIB from CALB wasFig. 3. (a) Initial SOC of each cell, (b) cell temperature, (c) cell voltage,and (d) cell current measurements following the application of the proposedbalancing method.utilized to perform EIS tests for these tests. In the beginning,the cells exhibited different initial SOC, as depicted in Fig.3(a). All maintain a uniform initial temperature of 25°C toformalize the testing conditions. Throughout the experimentalprocess, a constant external current of 50 Amps was appliedto simulate throughout the tests. As illustrated in Fig. 3(b)-(d),the cell temperatures, voltages, and currents are shown respec-Fig. 4. (a) PWM signal for controlling the switching of each cell, (b) SOCof each cell, and (c) SOC error from the average after applying the proposedbalancing method.tively after applying the proposed control strategy, providinginsightful details into the operational dynamics of each cellwithin the pack. Notably, the maximum temperature observedwas 33.1 °C, indicating a controlled thermal environment evensignificant by the proposed method.The visual representation of this process, as depicted inFig. 3 and Fig. 4, offers a clear understanding of the ANN’srole in dynamically managing cell characteristics for optimalbalance. The prosperous achievement of balanced cell results,as depicted through SOC values and their deviations fromthe average SOC in Fig. 4(b)-(c), highlights the precisionand efficiency of the intelligent balancing control strategy.The cells’ SOC convergence to the average, accomplishedwithin a relatively short span of 2500 seconds, stands as atest of the strategy’s effectiveness. This rapid convergenceis particularly notable, showcasing the system’s capability torectify imbalances and stabilize the pack’s operational statequickly. This outcome significantly validates the effectivenessof the proposed intelligent balancing approach, demonstratingits utility in managing complex battery pack dynamics andthe method’s ability to ensure balanced operation. Moreover,the successful implementation of this strategy underscores thepotential of ANN-based systems in advancing battery manage-ment technologies, opening avenues for further research anddevelopment in this domain.IV. CONCLUSIONThis paper proposed an advanced approach using ArtificialNeural Networks (ANN) for State of Charge (SOC) balancingin lithium-ion battery packs within the smart battery packframework. The study’s innovation lies in dynamically man-aging SOC through precise control of half-bridge switches,a significant step forward in battery management systems. Akey aspect of this approach was the selective bypassing ofindividual cells, which effectively enhanced SOC balancingand overall battery pack performance. The findings demon-strate the effectiveness of the proposed ANN-based balancingcontrol, characterizing it as a vital solution for improvingelectric vehicle battery systems.ACKNOWLEDGMENTThis work was funded by the “SMART BATTERY” project,granted by Villum Foundation in 2021 (project number222860).REFERENCES[1] X. Han, et al., A review on the key issues of the lithium ion batterydegradation among the whole life cycle, eTransportation 1 (2019),100005.[2] S.M. Lukic, J. Cao, R.C. 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Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

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Intelligent Cell Balancing Control for Lithium-Ion Battery Packs

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