Lithium-ion Battery Management System Research Articles

State of health estimation for lithium-ion batteries with Bayesian optimized Gaussian process regression

The estimation of the state of health (SOH) of lithium-ion batteries is of great importance to ensure the safe and stable operation of a lithium-ion battery management system (BMS). Accurate estimation of SOH can effectively prevent unnecessary losses of lithium-ion batteries due to failures. Therefore, a Bayesian-optimized Gaussian process regression (BO-GPR) method is proposed for SOH estimation. Firstly, the ageing characteristics reflecting the SOH of the battery are extracted from the charge/discharge time and incremental capacity (IC) curve, and the correlation between the health characteristics and the SOH is analysed by means of grey correlation (GRA). On the other hand, a Bayesian optimization algorithm is used to automatically find the optimal hyperparameters of the GPR model and trained using the battery dataset to obtain the SOH estimating model. To validate the effectiveness of the method, the datasets provided by NASA and Oxford are used as experimental objects and compared with different data-driven models to verify the accuracy and reliability of the proposed model. The experimental results show that the method proposed in this paper has a high accuracy in the estimation of the SOH, with the maximum average estimation error being within 1%.

Numerical Analysis of a Two-Layer PCM Based Battery Thermal Management System for Different Material Properties

The design and numerical analysis of the two-layer PCM (Phase Change Material)-based thermal management system for a 18650-type lithium-ion battery have been performed. In relation to simulation, the coefficient of thermal conductivity and melting temperature of the first layer of PCMs are varied. Other parameters are made identical to that of the next layer's parameters in order that the generation of two different layers of PCMs can be attained: PCM-1 and PCM-2. To obtain a more realistic approach in the numerical analysis, the battery thermal model was created in the COMSOL-MATLAB interface using the experimental internal resistance data obtained for 18650 type Li-ion batteries in the literature. While a cheaper and more accessible material with a thermal conductivity of 0.2 W/mK and a melting point of 50 °C was used in the PCM-2 layer, the thermal conductivity was changed to 0.2; 1 and 5 W/mK in the PCM-1 layer; and the melting point was changed to 30, 40 and 50 °C. In this way, for PCM layers with different thickness (tpcm), the system was optimized at two different discharge rates, 5C and 7C. As a result of the numerical analysis, it was determined that the optimum tpcm, kpcm,1 and Tm values for the 5C discharge rate were 2 mm, 0.2 W/mK and 40 °C, respectively; and the optimum tpcm, kpcm,1 and Tm values for the 7C discharge rate were 4 mm, 5 W/mK and 40 °C, respectively.

Building a better lithium-ion battery management system

Building a better lithium-ion battery management system

The novel stereoscopic cooling plate designs and performance analysis for battery thermal management systems

This study explores the design and performance of liquid cooling plate-based battery thermal management system for lithium-ion battery packs through numerical simulation. We first assess traditional straight-channel cooling plates with three different placement strategies. The conventional design with bottom-mounted cooling plates demonstrates the most effective thermal management at the same total coolant mass flow rate. This suggests that the influence of reducing coolant flow velocity on the cooling performance outweighs the influence of increasing the heat exchange area under our simulated conditions. Advanced stereoscopic cooling plate structures, referred to as 3DCP-A and 3DCP-B, are then introduced, significantly enhancing battery thermal management performance. To achieve a maximum temperature difference ≤ 5.0 K for the most discharge process, the required mass flow rate of the 3DCP-B decreases by 30.0 g∙s−1 and 10.0 g∙s−1 compared to the conventional bottom-mounted cooling plate and 3DCP-A, respectively, and the pressure drop reduces by 75.9 % and 44.3 %, respectively. The power consumption for the 3DCP-B design is only 6.0 % of that for the traditional design. Finally, the effects of key operating temperatures on the cooling performance of the optimal 3DCP-B design are discussed. The insights obtained in this study provide valuable guidance for developing more efficient and effective pack-level liquid cooling plate-based battery thermal management system, essential for improving the reliability and performance of electric vehicles.

Battery Lifetime Prediction and Degradation Analysis with Machine Learning Method

Due to environmental pollution and climate crisis caused by fossil fuels, eco-friendly alternative energy sources are gaining attention. Particularly, electric vehicles (EVs) are being recognized as the next-generation means of transportation. The most crucial technologies in electric vehicles are lithium-ion batteries and Battery Management Systems (BMS). However, the extended lifespan of lithium-ion batteries leads to prolonged assessment periods, making it challenging to measure and improve performance over time. Additionally, the operation of batteries under various conditions makes it difficult to measure state of charge (SOC) and state of health (SOH) in real-time, introducing many variables. With recent advancements in predictive technology through machine learning, accurate predictions can be made without complex physical knowledge, leveraging high-quality data.In this study, machine learning techniques were applied using charge/discharge data from lithium-ion LiNi0.5Mn0.3Co0.2O2 (NMC532)/graphite pouch cell batteries. The values measured during the initial charge/discharge cycles were converted into variables that could assess battery degradation modes. Machine learning algorithms were then applied, followed by model tuning to reduce overfitting and enhance accuracy. This research contributes to real-time battery management system data using machine learning technology to predict the remaining useful life (RUL) of lithium-ion batteries with high accuracy, using only initial cycles. This approach enhances the efficiency of BMS data in real-world scenarios and contributes to the overall time efficiency of battery research. Acknowledgement This research was supported by National R&D Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science and ICT(NRF-2021K1A4A8A01079455).

Estimating the Health State of Lithium-Ion Batteries Using an Adaptive Gated Sequence Network and Hierarchical Feature Construction

State of health (SOH) estimation plays a vital role in ensuring the safe and stable operation of lithium-ion battery management systems (BMSs). Data-driven methods are widely used to estimate SOH; however, existing methods often suffer from fixed or excessively high feature dimensions, impacting the model’s adjustability and applicability. This study first proposed a layered knee point strategy based on the charging voltage curve, which reduced the complexity of feature extraction. Then, a new hybrid framework called the adaptive gated sequence network (AGSN) model was proposed. This model integrated independently recurrent neural network (IndRNN) layers, active state tracking long short-term memory (AST-LSTM) layers, and adaptive gating mechanism (AGM) layers. By integrating a multi-layered structure and an adaptive gating mechanism, the SOH prediction performance was significantly improved. Finally, batteries under different operating conditions were tested using the NASA battery dataset. The results show that the AGSN model demonstrated higher accuracy and robustness in battery SOH estimation, with estimation errors consistently within 1%.

Expression of Concern for article entitled “Influence of mechanical vibration on composite phase change material based thermal management system for lithium-ion battery”

Expression of Concern for article entitled “Influence of mechanical vibration on composite phase change material based thermal management system for lithium-ion battery”

Active balancing system in battery management system for Lithium-ion battery

The existing battery management systems perform many functions, such as simply monitoring the battery's voltage, current, and temperature for the most basic and compensating energy imbalances between battery cells for the most advanced systems. In this last example, the function balancing helps protect the battery from obtaining a better lifespan. However, these systems with such functions remain complex because they involve techniques specific to power electronics and energy conversion. The number of components, implementation complexity, and cost increased. The work presented in this paper fits directly into this context. The main objective is to provide a solution to the problem of battery management and careful pack cell balancing. The proposed system aims to balance the battery pack cells based on the intermediate state of charge by charging or discharging the imbalanced cell. The implementation of the proposed control strategy was for a battery pack composed of five cells under MATLAB/Simulink.

Early Prognostics of Remaining Useful Life in Lithium Ion Batteries Using Hybrid LSTM-Att-MLP Model with Fusing Aging Information

Predicting the remaining useful lifetime (RUL) stands as a crucial aspect of lithium-ion battery management systems, acting as a core component of their functioning. Accurately predicting the RUL is essential for ensuring safety, preventing failures, and averting catastrophic incidents, but it is challenging, due to capacity degradation and aging effects. To overcome this, a hybrid model termed LAM: LSTM with an attention mechanism and MLP, for early RUL prediction, leveraging fused aging information is proposed. LSTM adeptly captures the significance embedded within feature sequences, preserving essential long-term features while effectively filtering out less pertinent information. Embedding attention mechanism with LSTM, the model dynamically focuses on different parts of the input sequence by assigning varying importance levels to different aging information, enhancing the prediction performance. For capturing the dynamic and nonlinear degradation trend of batteries and predicting RUL by effectively learning intricate degradation patterns MLP is utilized. The proposed model’s efficacy is evaluated using a NASA dataset through leave-one-out evaluation, utilizing 50% of the training data from three batteries to predict the others, and with varying starting points. The results indicate that under conditions of limited historical samples, the LAM attains higher accuracy and achieves minimum Mean Squared Error of 3.9962 × 10−5.

Research on the Design of a MIMO Management System for Lithium-Ion Batteries Based on Radiation–Conductivity–Convection Coupled Thermal Analysis

In this study, the heat transfer model of a radiation–conduction–convection coupled lithium-ion battery pack is established through theoretical analysis. The temperature distribution and flow field distribution inside the battery pack are obtained by simulation using ANSYS Fluent software 2022 R1, and the reasonableness of the simulation model is verified with an experiment. This study also analyzes in detail the improvement effect of adding heat dissipation ribs, applying heat dissipation coatings, and adjusting the fan speed on the heat dissipation performance of the system. Under the same heat sink rib height conditions, the relationship between its thickness and total heat dissipation and thermal efficiency is studied in depth, and the temperature distribution of the cell under different rib thicknesses is obtained. At the same time, the emissivity of the heat sink coating under different coating thicknesses was measured by infrared thermography, and the relevant design values were determined through simulation experiments. Finally, based on the experimental test results of fan performance, a corresponding control strategy is proposed to construct an efficient and high-performance multiple-input multiple-output (MIMO) battery thermal management system. The experimental results show that optimizing the structure of the forced air cooling system through the above measures can ensure that the Li-ion battery operates within the efficient operating temperature range, thus extending its cycle life, improving its stability, and reducing the risk of thermal runaway. Meanwhile, the problem of excessive temperature difference between different modules is improved, and the output capacity of the energy storage system is increased.

Predictive Set Point Modulation Control for Lithium-ion battery Management Systems with DC-DC Converters

Abstract Lithium-ion batteries, widely used in devices such as smartphones, remote controllers, and electric vehicles, often require adjustments to the output voltage to meet the varying demands of different devices. For instance, smartphones dynamically change the battery supply to enhance battery life, and electric vehicles necessitate different load voltages due to frequent start-stop cycles. This introduces the need for DC-DC converters to step up or step down the load voltage. However, this poses challenges to the stability of the bus voltage. In this study, we adopt a method for regulating the bus voltage reference value to predict and stabilize the voltage supplied by lithium-ion batteries. We first model the power supply system of lithium-ion batteries and then design a cascaded control framework for the voltage of DC bus. We introduce a predictive set-point control approach that eliminates the necessity to modify the energy storage system to mitigate voltage fluctuations. Simulation results illustrate that the proposed method effectively mitigates voltage overshoots in the DC bus.

Simulation of health assessment model for thermal management system of lithium-ion battery based on fuzzy clustering algorithm

Considering the inadequate assessment of the factor decline index in the thermal management system for lithium-ion batteries, which has a negative impact on their efficient utilization, a fuzzy clustering algorithm is suggested to create a simulation model for evaluating the health of the lithium-ion battery thermal management system. In the process of restoring characteristic data by the fuzzy clustering algorithm, according to the characteristic parameters of energy signal and power signal, the decay characteristic results of the lithium-ion battery thermal management system are extracted. Taking the minimum attenuation as a reference, the evaluation function is calculated, the health trend of the lithium-ion battery thermal management system is evaluated, the health grade is established, and the health evaluation model of the lithium-ion battery thermal management system is constructed. The experimental results show that the prediction of the remaining service life of five kinds of lithium-ion batteries by this model is consistent with the actual value, and the average absolute error is always within 0.02, which is superior in health assessment.

A novel least squares support vector machine-particle filter algorithm to estimate the state of energy of lithium-ion battery under a wide temperature range

The state of energy (SOE) is a key indicator for lithium-ion battery management systems (BMS). Based on the second-order resistance-capacitance equivalent circuit model and online parameter identification using the dynamic weights particle swarm optimization (DWPSO) method, a least-squares support vector machine-particle filter (LSSVM-PF) algorithm is proposed to construct a particle filter to estimate the SOE of a lithium-ion battery, and then transfer the resulting estimation error together with the experimentally measured voltage and current values to a trained LSSVM model, and use the LSSVM model to optimize the SOE estimates obtained by the PF algorithm twice to improve the accuracy of SOE estimation for lithium-ion batteries. The feasibility of the proposed algorithm is verified using two complex operating conditions and at three different temperatures. The validation results show that the maximum error of SOE estimation of the proposed algorithm is 0.0284 for a wide temperature range under Beijing Bus Dynamic Stress Test (BBDST) condition, and 0.0226 for a wide temperature range under Dynamic Stress Test (DST) condition. The proposed algorithm significantly improves the accuracy of SOE estimation and provides a reference for fundamental applications of lithium-ion batteries.

Programmable logic controlled lithium-ion battery management system using passive balancing method

Advancements in electric vehicle technologies and the widespread adoption of electric vehicles have brought energy storage systems to the forefront. However, efficient and secure utilization of energy storage systems requires Battery Management Systems (BMS).BMSs are typically designed with power electronics, electronic cards, integrated circuits, and auxiliary hardware components. Nevertheless, BMSs designed with such microcontrollers can only control a limited number of battery cells and constrained current values. Furthermore, employing a separate controller for each battery pack exacerbates these issues.In this study, a Programmable Logic Controller (PLC) - based BMS proposal for lithium-ion batteries has been presented, aiming to address the challenges in existing BMSs. The developed system is a passive balancing BMS comprised of controller PLC modules and auxiliary hardware. The Ladder Diagram (LD) programming language was employed for programming the PLC.In the conducted research, the voltage levels of the cells comprising the battery pack were monitored, and a passive balancing method was employed to equalize the cell voltages. Passive balancing has been applied to cells with voltage values of 2.70 V, 2.40 V, and 2.80 V using the minimum reference voltage cell balancing method to equalize the voltage levels. The output voltage has been balanced according to the maximum reference charge voltage set at 6 V through the maximum reference charge voltage balancing method. Additionally, a cooling circuit was integrated into the system to safeguard the Battery Management System (BMS) against thermal hazards. The developed PLC controller and algorithm can effectively control multiple battery packs with a single controller and operate at higher current values. This advantage allows the PLC-controlled BMS to have a broader control capacity. The presented test results indicate the successful performance of the PLC-based BMS.

Comprehensive Comparative Analysis of Deep-Learning-Based State-of-Charge Estimation Algorithms for Cloud-Based Lithium-Ion Battery Management Systems

Comprehensive Comparative Analysis of Deep-Learning-Based State-of-Charge Estimation Algorithms for Cloud-Based Lithium-Ion Battery Management Systems

An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration

Accurate prediction of remaining useful life (RUL) is crucial to the safety and reliability of the lithium-ion battery management system (BMS). However, the performance of a lithium-ion battery deteriorates nonlinearly and is heavily affected by capacity-regeneration phenomena during practical usage, which makes battery RUL prediction challenging. In this paper, a rest-time-based regeneration-phenomena-detection module is proposed and incorporated into the Coulombic efficiency-based degradation model. The model is estimated with the particle filter method to deal with the nonlinear uncertainty during the degradation and regeneration process. The discrete regeneration-detection results should be reflected by the model state instead of the model parameters during the particle filter-estimation process. To decouple the model state and model parameters during the estimation process, a dual-particle filtering estimation framework is proposed to update the model parameters and model state, respectively. A kernel smoothing method is adopted to further smooth the evolution of the model parameters, and the regeneration effects are imposed on the model states during the updating. Our proposed model and the dual-estimation framework were verified with the NASA battery datasets. The experimental results demonstrate that our proposed method is capable of modeling capacity-regeneration phenomena and provides a good RUL-prediction performance for lithium-ion batteries.

Lithium-ion battery thermal management system using MWCNT-based nanofluid flowing through parallel distributed channels: An experimental investigation

In the present research, a newly designed liquid-based battery thermal management system (BTMS) for lithium-ion batteries (LIBs) is developed and its thermal performance at different discharge rates (C-rates) is experimentally examined. The influence of various performance parameters such as the number of channels, flow configurations, working fluids, and C-rates on the cooling performance of the proposed BTMS is evaluated. Pure water, binary fluid (70 % water + 30 % ethylene glycol (EG)), and multi-walled carbon nanotubes (MWCNTs)-based nanofluids at different volume fractions (ϕv) (0.15 %, 0.30 %, and 0.45 %) in the binary fluid are used in this study and their cooling effectiveness to keep battery temperature within ideal ranges is compared. The experiments are performed at 0.5C, 1.2C, and 2.1C rates and it is discovered that nanofluids provide superior cooling performance compared to water and binary fluid mixture. Results demonstrate that the cooling performance is enhanced with the number of channels, nanoparticle concentration, and flow configurations. Nanofluid with 0.45 % ϕv of MWCNTs shows the maximum drop in battery temperature which is about 8.7 °C for configuration I, 11.8 °C for configuration II, and 17.1 °C for configuration III, respectively. Among all the flow configurations, configuration III gives the best cooling effectiveness and lowers the average temperature of cells by 10–17.01 °C using different working fluids. Configuration III also enhances the battery temperature uniformity and maintains the maximum temperature deviation within the battery module (∆Tmax) inside 1.5–2.4 °C for different working fluids at a 2.1C rate, which is well below 5 °C, and hence it helps to prevent the thermal runaway (TR) condition. Additionally, nanofluid with 0.45 % ϕv increases pressure drop (Δp) by 23.98 % and 17.91 % compared to water for single and dual channel arrangements, respectively.

Integrating Level Shift Anomaly Detection for Fault Diagnosis of Battery Management System for Lithium-Ion Batteries

Integrating Level Shift Anomaly Detection for Fault Diagnosis of Battery Management System for Lithium-Ion Batteries

A Comprehensive Review of Cloud-Based Lithium-Ion Battery Management Systems for Electric Vehicle Applications

A Comprehensive Review of Cloud-Based Lithium-Ion Battery Management Systems for Electric Vehicle Applications

Experimental study on the effect of phase change material on thermal runaway characteristics of lithium-ion battery under different triggering methods

Although paraffin (PA) is a commonly used phase change material (PCM) for lithium-ion battery thermal management, its flammability may increase the risk of thermal runaway. This paper investigates the influence of PA on the thermal runaway, and the highly effective flame retardant hexa-phenoxy-cyclotriphosphazene (HPCP) was employed to suppress PA combustion and mitigate the thermal runaway hazard. Penetration and heating were used to induce thermal runaway. The battery surface temperature, the material temperature and the flame condition were recorded. The results show that penetration caused more violent reactions than heating, the battery temperature of penetration was 25 % higher than that of heating, while heating led to more harmful flame with a flame temperature over 1000 °C. PA delayed the onset of thermal runaway by 58.7 %, however, the higher temperature and longer burning time were a sign of increased danger. HPCP inhibited PA combustion, reducing the burn time by 35.7 %, while HPCP/PA delayed thermal runaway and controlled the battery flame, with a minimum of 43.5 % reduction in flame height. This study may provide a reference for the safe design of thermal management system for lithium-ion battery.