Battery Management Systems for Electric Vehicles

Abstract Lithium-based batteries (LiBs) are integral components in operating electric vehicles to renewable energy systems and portable electronic devices, thanks their unparalleled density, minimal self-discharge rates, favorable cycle life. However, the inherent safety risks performance degradation of LiB over time impose continuous monitoring facilitated by sophisticated battery management (BMS). This review comprehensively analyzes current state sensor technologies for smart LiBs, focusing on advancements, opportunities, potential challenges. Sensors classified into two primary groups based application: optimization. Safety sensors, including temperature, pressure, strain, gas, acoustic, magnetic focus detecting conditions that could lead hazardous situations. Performance optimization such as optical-based electrochemical-based, monitor factors charge health, emphasizing operational efficiency lifespan. The also highlights importance integrating these sensors with advanced algorithms control approaches optimize charging discharge cycles. Potential advancements driven nanotechnolo... Read More

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The global shift toward environmentally sustainable transportation has accelerated the development and adoption of Battery Electric Vehicles (BEVs). This paper presents a comprehensive evaluation BEVs using Estimation Based on Distance from Average Solution (EDAS) method, multi-criteria decision-making (MCDM) approach that facilitates an objective systematic comparison across multiple performance parameters. assessment considers critical technical user-centric criteria, including battery capacity, driving range, top speed, acceleration (0100 km/h), charging time, overall environmental impact. Eight popular BEV modelsKia EV6, Mahindra XUV400 EV, Hyundai Kona Electric, BMW i7, Jaguar I-Pace, Mercedes-Benz EQS, Audi e-tron GT, Porsche Taycanwere analyzed EDAS method to determine their relative scores. analysis revealed Kia EV6 ranks highest, demonstrating well-balanced profile all key indicators. Conversely, recorded lowest score due trade-offs in metrics. results offer practical implications for potential consumers, manufacturers, policymakers by identifying optimal models hig... Read More

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LiFePO4 batteries need a battery management system (BMS) to improve performance, extend their lifespan, and maintain safety by utilizing advanced monitoring, control, optimization techniques. This paper presents the design, development, implementation of an intelligent (i-BMS) that integrates real-time monitoring control batteries. The was extensively tested using multiple datasets, results show able temperature within set range, balance cell voltages, distribute energy according load prioritization. It uses fuzzy logic approach effectively manage farm requirements. Additionally, proposed method embedded three-level prioritization algorithm woven into rule allocate dynamically among essential, regular, non-essential loads.

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Accurate estimation of the state charge (SOC) lithium-ion batteries (LiBs) proportionally impacts efficiency battery management systems (BMS) considering dynamic and non-linear behavior LiBs. Changes in activities cathode anode materials internal resistance tend to impact capacity. When is operated at high or low temperatures under HWFET condition, capacity tends deteriorate drastically. Therefore, high-precision SOC required ensure safe stable operation. In this work, we propose a combined Improved Dung Beetle Optimization (IDBO) Extreme Learning Machine (ELM) framework for evaluate BMS. The novelty model stems from application IDBO algorithm, which incorporating Circle chaotic mapping, Golden sine strategy, Levy flight hyper-parameter optimization. This effectively resolves problems inconsistent performance instability arising randomly initialized hidden layer weights biases ELM, resulting enhanced prediction accuracy. proposed IDBO-ELM method validated context five parameters, namely, different ambient temperatures, operating conditions, materials, initial values, running time. ex... Read More

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Abstract The multistage constant current (MCC) charging protocol for lithiumion batteries is commonly used to balance lithium plating and time. Traditional methods depend on a predefined map without considering the feedback of subsequent selfregulation rate. To tackle this problem, an adaptive MCC method proposed, which based expansion force detect plating. By integrating experiments with simulations, results indicate that when occurs, experiences abnormal, accelerated increase. If rate reduced until ceases, decreases. Correspondingly, three thresholds, V1, V2, V3, in derivative (dF/dSOC), are identified. Utilizing these can be selfregulated. demonstrate speed increased by 50% causing irreversible proposed holds great promise integration into intelligent battery management systems, thereby enhancing performance fast charging.

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This paper shows that lithium-ion battery model parameters are vital for state-of-health assessment and performance optimization. Traditional evolutionary algorithms often fail to balance global local search. To address these challenges, this study proposes the Elite Opposition-Based Learning Snake Optimization (EOLSO) algorithm, which uses an elite opposition-based learning mechanism enhance diversity a non-monotonic temperature factor exploration exploitation. The algorithm is applied parameter identification of second-order RC equivalent circuit model. EOLSO outperforms some traditional optimization methods, including Gray Wolf Optimizer (GWO), Honey Badger Algorithm (HBA), Golden Jackal (GJO), Enhanced (ESO), (SO), in both standard functions HPPC experiments. experimental results demonstrate significantly SO, achieving reductions 43.83% Sum Squares Error (SSE), 30.73% Mean Absolute (MAE), 25.05% Root Square (RMSE). These findings position as promising tool modeling state estimation. It also potential applications management systems, electric vehicle energy management, other compl... Read More

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Abstract Batteries are commonly connected in series and parallel to create modules that fulfill the power energy requirements of specific applications. However, conclusions about battery performance degradation under different conditions, as well predictive models, often derived from single cell cycling results. In this study, we evaluate six series-parallel configurations commercial lithium nickel manganese cobalt cells over hundreds cycles. Each within was individually instrumented for voltage, current, temperature monitoring. We quantified impact module configuration on overall throughput, voltage-spread among series-connected cells, current heterogeneity parallel-connected cells. This one broadest reported date, supports systematic evaluation trade-offs, pack penalty, safety implications configurations.

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Safety concerns in lithiumion batteries pose significant challenges for electric vehicle systems. A reliable battery thermal management system is essential to maintain optimal performance. In this article, simulation carried out the design of aircooled packs with aligned, equally spaced staggered, and nonequally staggered arrangements, based on experimental validation. The spacing positioning are optimized cooling heating conditions determine heat dissipation configuration. results reveal that arrangements enhance transfer, configuration reducing maximum temperature difference by 15.6% energy consumption 36.6%, compared setup. Furthermore, considering control demands pack wind speed, state equation model predictive constructed solved particle swarm optimization algorithm. Subsequently, cosimulation employed verify its effectiveness. proposed strategy efficiently regulates reduces consumption, demonstrating potential improving practical applications.

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Multisine electrochemical impedance spectroscopy (EIS) represents a highly promising technique for the online characterization of battery functional states, offering potential to monitor, in real-time, key degradation phenomena such as aging, internal resistance variation, and state health (SoH) evolution. However, its widespread adoption embedded systems is currently limited by need balance measurement accuracy with strict energy constraints requirement short acquisition times. This work proposes novel broadband EIS approach based on multiband multisine excitation strategy which signal spectrum divided into multiple sub-bands that are sequentially explored. enables available be concentrated portion at time, thereby significantly improving signal-to-noise ratio (SNR) without substantially increasing total time. The result more energy-efficient method maintains high diagnostic precision. We further investigated optimal design these sequences, taking account realistic imposed sensing hardware limitations amplitude noise level. effectiveness proposed was demonstrated within comprehensiv... Read More

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Battery management system for electric vehicles with a refined voltage measurement technique to optimize battery performance and durability. The system manages an electric battery device with multiple modules connected in series, each containing cells. Some cells are connected by a busbar. The measurement technique accounts for busbar voltage offsets. It uses a single slave element to gather data from two modules. Measurements from cells on the busbar are adjusted based on the busbar resistance. This prevents overestimation due to busbar voltage. The adjusted cell voltages are used for safety methods like derating charging power. The technique improves accuracy by avoiding erroneous voltage readings from busbar cells.

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This study examines the thermal behaviour of a LiFePO battery pack in converted electric vehicle using GT-Suite simulation. The base vehicle, Toyota Avanza originally powered by 1.3-litre engine, was retrofitted with 60 kW synchronous AC motor and 268.8 V, 40.32 kWh pack. Simulations were conducted under NEDC WLTP driving cycles, both without passive cooling. Results showed that temperature peaked at 45.3C (NEDC) 71.6C (WLTP) cooling, reduced to 36.6C 48.0C respectively Temperature spikes coincided rapid acceleration high-speed phases, highlighting influence discharge current on heating. These findings demonstrate importance management EV conversions effectiveness Future work will focus experimental validation control via system (BMS) ensure safety longevity.

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Battery module housing with directed and controlled venting for lithium-ion battery packs in electric vehicles. The housing has two types of vents: burst vents to rapidly release excess gas buildup during cell operation, and selective permeability vents to slowly allow gas exchange between the cell stack and housing cavity. This enables directed venting of excess gases away from the vehicle cabin while still allowing controlled breathing of the cells. The burst vents open at high pressure thresholds, while the selective permeability vents have membranes that allow gas exchange but not moisture.

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Method for accurately estimating the initial state of charge of a battery in a hybrid or electric vehicle, particularly when the battery has not been at rest for a long time. The method involves estimating an initial value of the state of charge during an initialization phase. The initialization phase includes steps like measuring the battery's voltage and current at the start of charging or discharging, calculating the current polarization based on the voltage and current values, and using the polarization to adjust the initial state of charge estimate. This takes into account the battery's polarization state to provide a more accurate initial state of charge estimate when the battery has not been resting for a long time.

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Thermal management system for electric vehicle battery packs that provides efficient cooling and heating without adding significant weight or cost. The system uses a network of flexible tubes connecting intake and exhaust manifolds with channels tuned for even fluid flow distribution. It allows direct contact cooling/heating of individual battery cells by conforming tubes passing between them. The system connects to a pump and heat exchanger for circulating fluid through the pack. The flexible tubes fill and bleed easily for installation. The manifold design prevents fluid bypassing and ensures full inflation.

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Rechargeable battery monitoring system for electric vehicles that prevents damage to battery management unit (BMU) chips when disconnecting/connecting manual service disconnect (MSD) switches. The system has multiple cell monitoring circuits (CMCs) connected in groups via daisy chain buses to the BMU. The CMC groups are separated by MSD switches between adjacent battery unit groups. By balancing the number of battery units and CMCs in each group, the daisy chain length and line losses are equalized. Isolation transformers between adjacent CMCs further improve isolation and signal quality. This reduces voltage spikes and current surges when disconnecting/connecting MSDs that could damage CMC and BMU chips.

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Battery module design to prevent thermal runaway propagation in stacked battery cells. The module has thermal runaway preventers between the cells that disperse heat from an overheating cell and prevent direct transfer to neighboring cells. This prevents thermal runaway chain reactions that can occur when one cell overheats. The preventers are installed in the module case between the cells in the stack.

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Simplified and accurate method to measure battery resistance using existing vehicle components. It leverages the fact that when a battery is charging and discharging simultaneously, it outputs more current than it receives. By determining if this condition is met, the method can detect when the battery switches from a charged to discharged state. At this point, it starts measuring the resistance. This avoids the need for specialized equipment and allows using the vehicle's control system to enable battery resistance measurement during normal operation.

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Method and controller for accurately detecting voltages of battery cells in a battery pack using converters and switching units. The method involves routing the operating current from the battery cell anode through the converter to ground, while also routing a sampling current from the battery cell anode to the converter. This reduces the difference between currents through the anode and cathode paths, allowing the converters to accurately detect the cell voltage.

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Improving safety and reliability of battery packs by selectively supplying power to the cooling system when a thermal runaway occurs in one module. When a battery module undergoes thermal runaway, the system determines the module and surrounding modules without runaway based on voltage levels. It then powers the chiller using the non-runaway modules to cool the pack. This prevents overheating and gas generation in the runaway module. The vehicle control unit assists by checking the power supply circuit of the backup modules.

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Battery module and power system design to mitigate wireless communication interference and leakage in electric vehicle battery packs. The module has individual battery cells with detectors and communicators inside an electromagnetic shielded housing. The housing has a non-shielded portion for access. An electromagnetic shield opposite the non-shielded portion prevents wireless signals from leaking or entering. This isolates the wireless communication inside the shielded space.

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