Advanced Battery Management Systems Research Articles

ELECTRIC VEHICLE POWERTRAIN DESIGN: INNOVATIONS IN ELECTRICAL ENGINEERING

The electrification of vehicles is reshaping transportation, with electric vehicle (EV) powertrain design playing a key role in this shift. This review focuses on recent innovations in electrical engineering that have enhanced EV powertrains, particularly in areas like electric motors, power electronics, energy storage, and thermal management. Advances in motor technology, such as permanent magnet synchronous motors (PMSM), and the integration of silicon carbide (SiC) and gallium nitride (GaN) semiconductors have improved efficiency and reduced heat generation. Battery innovations, including solid-state technologies and advanced battery management systems, along with regenerative braking, have extended EV range and efficiency. Power electronics, including inverters and onboard chargers, now utilize wide-bandgap semiconductors to minimize energy losses and improve thermal performance. Additionally, novel cooling solutions are addressing thermal challenges in EV systems. Looking forward, modular powertrain architectures and AI-driven control strategies offer promising advancements for various vehicle types. This review provides an overview of how electrical engineering innovations are driving the future of EV powertrain design and sustainable transportation.

Effect of Cell-to-Cell Internal Resistance Variations on the Thermal Performance of Lithium-Ion Batteries for Urban Air Mobility

This study examines the thermal behavior of lithium-ion battery modules intended for Urban Air Mobility (UAM), a forthcoming urban transport system designed to facilitate efficient and secure passenger and cargo transport within city centers. UAM applications necessitate batteries with high energy densities capable of sustaining elevated discharge rates during critical phases such as takeoff and landing. The battery module evaluated in this study comprises four cells arranged in series and configured as a submodule for UAM applications. A three-dimensional thermal model was utilized to analyze the impact of external temperature fluctuations and high discharge rates on the performance of the battery module. The numerical findings indicated considerable variations in temperature and internal resistance among the cells, especially under high discharge rates at low temperatures, with a maximum temperature deviation of 32.952 °C observed at an 8 C discharge rate. These thermal non-uniformities were attributed to variations in cell capacity and internal resistance, which were amplified by manufacturing inconsistencies and operational conditions. The study underscores the necessity of robust thermal management strategies to mitigate the risk of thermal runaway and ensure the operational safety and reliability of UAM systems. The results emphasize the critical role of advanced Battery Management Systems (BMS) in monitoring and controlling cell voltage and temperature to achieve consistent performance across the battery module. This research contributes valuable insights into the design of more efficient and reliable battery modules for UAM, highlighting the importance of addressing cell-to-cell performance discrepancies to enhance overall module efficacy and durability.

Nonlinear observation of battery microscopic states utilizing adaptive super-twisting sliding-mode observers based on a compact electrochemical-thermal-aging model

Accurate microscopic-state estimation of lithium-ion batteries (LIBs) is a potential candidate for advanced battery management systems. This paper proposes an adaptive super-twisting sliding-mode observer (SMO) for LIB microscopic state perception using a compact and observable electrochemical-thermal-aging model. Considering the physical structure and mathematical model, a simplified single-particle mode with electrolyte is standardized as a compact state-space form after observability analysis. Results show that the compact model is capable of capturing the LIB terminal voltage and temperature with maximum errors of 39.8 mV and 0.2 °C, respectively. After that, the super-twisting SMO is integrated with a neural network-based adaptive law for addressing external disturbances and chattering. Estimation results show that during the majority of cycles, the errors of state of charge, volume-averaged concentration, temperature, and capacity are <3 %, 0.87 kmol/m3, 0.2 °C, and 0.1 Ah, respectively. The side-reaction overpotential of lithium plating prevention can be predicted. Comparable experiments with different observers and hardware-in-the-loop experiments are conducted to explore the estimation accuracy and real-time feasibility of the adaptive observer.

Estimating Parameters of Physics-Based Battery Models – What Machine Learning Can and Cannot Do

With the widespread use of Lithium-ion (Li-ion) batteries in multiple industries, the design space for the electrochemical cells has increased drastically. Li-ion batteries’ design parameters and material properties, such as porosity, electrode thickness, active material loading, and solid phase diffusivities, typically vary substantially due to different design goals and variation in the manufacturing process. Due to the difficulty in obtaining these and other battery cell parameters, many battery management systems used to control and monitor Li-ion batteries opt for the simplified resistor-capacitor circuit-based battery cell representation over using physics-based battery models. However, if these parameters become obtainable, scientists could implement advanced battery management systems using physics-based battery models to capture precisely how the battery performs and degrades in a wide variety of applications. Multiple methods, such as genetic algorithms, statistics, and machine learning, have been attempted for the parametrization and characterization of battery cells to enable accurate battery simulations using physics-based models [1, 2]. However, there are inherent limitations to the type and number of parameters that can be estimated using these methods if only the standard battery cell cycling data is utilized for this purpose. The present work elucidates and quantifies these limitations specifically in the case of machine learning based approach and provides deeper insights into these limitations that may help develop more robust cell characterization protocols. In the present work, model parameters are varied in a continuum-level physics-based model and simulations are performed for: (1) constant current-constant voltage (CC-CV) charging and constant current discharge at different C-rates, and (2) electrochemical impedance spectroscopy (EIS) in the relevant frequency range at multiple states of charge (SOCs). The Single Particle Model (SPM) is used for performing these simulations due to its relative simplicity and small number of parameters. At first, the charge and discharge data are input as training data in a long short-term memory neural network (LSTM NN), and EIS data is input as training data in a convolutional neural network (CNN). Once trained, the machine learning (ML) model uses charge, discharge, and EIS data withheld from the original training data set to predict SPM parameters. The estimated parameters are compared with the true parameters used for SPM charge, discharge, and EIS simulations, and the error in parameter estimation is quantified. A rigorous grid convergence analysis is performed to highlight the importance of ensuring that the spatial discretization error of the model does not influence the parameter estimation accuracy. For the first error quantification, all parameters are varied concurrently to obtain the parameter estimation error that arises from a large, undetermined parameter set. Parameters are then analyzed individually and in clusters related to diffusion, kinetics, and stoichiometric limitations. Further analysis is performed on parameters that could not be estimated with sufficient accuracy, including theoretical explanations for the incurred error and the effect of this error on the internal state predictions.

Distributed Generation Approach with Helping of Charging Stations

This study addresses the optimization difficulties created by the combination of distributed generation (DG) and charging stations (CS), with the key difference that charging stations not only permit electric vehicle charging but also actively contribute to power supply. Using Convex Optimization (CVX) methods, the study formulates and solves optimization issues for coordinating and controlling DG units, taking into account charging stations' dual roles as power suppliers. The findings indicate that EVs can significantly contribute to grid stability, but challenges such as battery degradation and the need for advanced battery management systems (BMS) must be addressed to optimize these benefits. Simulation results show a potential improvement in grid stability the promise and the practical considerations of this innovative approach.

Powering the Future: Advanced Battery Management Systems (BMS) for Electric Vehicles

This paper introduces a novel approach for rapidly balancing lithium-ion batteries using a single DC–DC converter, enabling direct energy transfer between high- and low-voltage cells. Utilizing relays for cell pair selection ensures cost-effectiveness in the switch network. The control system integrates a battery-monitoring IC and an MCU to oversee cell voltage and ensure battery protection. A prototype circuit with twelve lithium-ion batteries demonstrates the method’s efficacy, achieving a remarkable balancing time of 48 min during charging with a maximum efficiency of 89.85%. Comparative analysis with other methods underscores the superior performance of the proposed balancing circuit in terms of balancing time and implementation cost. Furthermore, this paper delves into hardware aspects of battery management systems (BMSs) for electric vehicles and stationary applications. It offers an overview of prevailing concepts in state-of-the-art systems, aiding readers in assessing considerations essential for BMS design in various applications. The discussion includes examples of battery packs sourced from commercially available electric vehicles. Subsequently, the manuscript addresses implementation aspects concerning the measurement of critical physical variables such as voltage, current, and temperature, alongside balancing strategies.

Temperature-dependent hysteresis model for Li-ion batteries

The increasing importance of accurate battery state estimations in advanced Battery Management Systems (BMS) underscores the need for precise modelling of battery behaviour and characteristics. While equivalent circuit models are widely utilized for their low computational demands, they face challenges in maintaining precision and adaptability during dynamic conditions, posing a persistent concern for future advancements. This study focuses specifically on the battery hysteresis effect, a complicating factor in the modelling and estimation processes. Open circuit voltage (OCV) measurements and parameter identification for equivalent circuit models were conducted on prevalent Li-ion battery technologies, namely nickel-manganese–cobalt (NMC) and lithium-iron-phosphate (LFP). The experimental results indicate the hysteresis effect becomes more significant with lower temperatures. In this paper, a battery model covering the temperature influence on the hysteresis effect is proposed. The proposed model exhibits an average root mean square error of less than 13 mV. The model holds promise for application in modern battery management systems, offering an enhancement to state-of-charge estimation methodologies.

The safety and environmental impacts of battery storage systems in renewable energy

The integration of battery storage systems in renewable energy infrastructure has garnered significant attention due to its potential to enhance energy reliability, efficiency, and sustainability. However, alongside these benefits, concerns persist regarding the safety and environmental impacts associated with the deployment and operation of such systems. This review explores the multifaceted aspects of safety and environmental considerations in battery storage systems within the context of renewable energy. Firstly, safety concerns encompass a range of factors, including thermal runaway, fire hazards, and chemical leakage, which pose risks to both human life and property. Mitigation strategies such as advanced battery management systems and fire suppression technologies are critical for addressing these risks effectively. Secondly, environmental impacts arise throughout the lifecycle of battery storage systems, from raw material extraction to end-of-life disposal. Key issues include resource depletion, greenhouse gas emissions, and pollution from mining activities. Sustainable practices such as responsible sourcing of materials, recycling initiatives, and the development of second-life applications are essential for minimizing environmental footprints. Furthermore, the interaction between battery storage systems and renewable energy sources introduces complexities in assessing environmental impacts. While battery storage facilitates the integration of intermittent renewables like solar and wind by providing grid stabilization and energy storage capabilities, its environmental benefits may be compromised by factors such as energy-intensive manufacturing processes and reliance on non-renewable resources. The safety and environmental impacts of battery storage systems in renewable energy demand comprehensive evaluation and management strategies to maximize benefits while minimizing risks. Collaboration among stakeholders, technological innovation, and regulatory frameworks are crucial for promoting the sustainable deployment and operation of these systems in the transition towards a cleaner and more resilient energy future.

A Case Study of a Tiny Machine Learning Application for Battery State-of-Charge Estimation

Growing battery use in energy storage and automotive industries demands advanced Battery Management Systems (BMSs) to estimate key parameters like the State of Charge (SoC) which are not directly measurable using standard sensors. Consequently, various model-based and data-driven approaches have been developed for their estimation. Among these, the latter are often favored due to their high accuracy, low energy consumption, and ease of implementation on the cloud or Internet of Things (IoT) devices. This research focuses on creating small, efficient data-driven SoC estimation models for integration into IoT devices, specifically the Infineon Cypress CY8CPROTO-062S3-4343W. The development process involved training a compact Convolutional Neural Network (CNN) and an Artificial Neural Network (ANN) offline using a comprehensive dataset obtained from five different batteries. Before deployment on the target device, model quantization was performed using Infineon’s ModusToolBox Machine Learning (MTB-ML) configurator 2.0 software. The tests show satisfactory results for both chosen models with a good accuracy achieved, especially in the early stages of the battery lifecycle. In terms of the computational burden, the ANN has a clear advantage over the more complex CNN model.

Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.

Robust state of charge estimation for lithium-ion batteries in variable temperatures using truncated singular value decomposition Cubature Kalman Filter algorithm

Advanced battery management systems play a significant role in the safe and efficient use of battery energy storage systems. While the accurate and reliable estimation of state of charge (SOC) is essential for improving the utilization efficiency of batteries and extending their service life. However, the working environment of battery energy storage systems is highly complex, and temperature changes can often affect the accuracy of modeling and estimation. The traditional methods of SOC estimation often ignore the changes in temperature of the whole system, thus resulting in the cumulative errors in SOC estimation. To address this problem, an improved method is proposed in this paper to estimate the SOC of lithium-ion batteries. It requires the use of Cubature Kalman Filter (CKF) algorithm that is based on Truncated Singular Value Decomposition (TSVD). Additionally, the Forgetting Factor Recursive Least Squares (FFRLS) method is used to determine the parameters of the Thevenin equivalent model for lithium batteries. This algorithm is then validated through simulation analysis and comparison with the experimental data obtained under cyclic operating conditions at a temperature of 5 ℃, 25 ℃, and 40 ℃, respectively. The results demonstrate that the proposed TSVD-CKF algorithm is accurate in reflecting the impact of ambient temperature on the model parameters and is applicable to SOC estimation in a wide range of temperatures, showing a strong robustness.

Advanced battery management systems: An in-depth comparative study

The research focuses on doing a thorough comparative analysis of different Battery Management Systems (BMS) used in modern battery technologies. Its goal is to completely examine and identify performance differences across several important parameters. Empirical data analysis reveals substantial disparities across batteries for crucial factors. The battery specs showed a range of capacities, with Battery B003 having the maximum capacity of 120 Ah and Battery B002 having the lowest capacity of 85 Ah. The temperature performance testing revealed significant variances in operating temperatures, with B003 exhibiting the widest temperature range of -20 to 50°C. Significant variations in the rates of charging and discharging were noted, with B004 exhibiting the greatest rates. Furthermore, there were significant differences in efficiency and aging properties. Specifically, B005 had the greatest efficiency, reaching 97%, and the lowest deterioration rate, which was just 0.09%. These data highlight significant variations across batteries, stressing the need for customized BMS techniques. The comparison study provides crucial insights into the complex intricacies of battery behavior, offering fundamental information essential for creating efficient BMS designs. Comprehending these differences is crucial for improving battery management techniques, guaranteeing effective and secure battery operations in various applications, and directing future progress in energy storage systems for electric cars, renewable energy, and portable devices.

Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities

In order to reduce carbon emissions and address global environmental concerns, the automobile industry has focused a great deal of attention on electric vehicles, or EVs. However, the performance and health of batteries can deteriorate over time, which can have a negative impact on the effectiveness of EVs. In order to improve the safety and reliability and efficiently optimize the performance of EVs, artificial intelligence (AI) approaches have received massive consideration in precise battery health diagnostics, fault analysis and thermal management. Therefore, this study analyzes and evaluates the role of AI approaches in enhancing the battery management system (BMS) in EVs. In line with that, an in-depth statistical analysis is carried out based on 78 highly relevant publications from 2014 to 2023 found in the Scopus database. The statistical analysis evaluates essential parameters such as current research trends, keyword evaluation, publishers, research classification, nation analysis, authorship, and collaboration. Moreover, state-of-the-art AI approaches are critically discussed with regard to targets, contributions, advantages, and disadvantages. Additionally, several significant problems and issues, as well as a number of crucial directives and recommendations, are provided for potential future development. The statistical analysis can guide future researchers in developing emerging BMS technology for sustainable operation and management in EVs.

Aging-Sensitive Fast-Charging for Heavy-Duty Electric Vehicles

To use lithium-ion cells on-board electric vehicles (EVs) safely and efficiently, a battery management system (BMS) is essential. It monitors the battery’s states and controls utilization by limiting e.g., current and temperature. Advanced BMS increasingly use electrochemical models to assess internal states of battery cells and apply usage constraints based on these [1]. The performance of an electrochemical BMS is closely tied to the performance of the underlying model and the accuracy of its parameters [2]. We employ a novel re-parametrization method to update electrochemically constrained optimal fast-charging procedure to minimize battery degradation. The parameters of electrochemical models are typically identified through a combination of physico-chemical characterization techniques and curve fitting [3]. Since model parameters change with aging [4], there is a need to re-parametrize electrochemical models on-board to conserve model accuracy. As basis for this re-parametrization, we propose a novel reference performance test that can be incorporated into a conventional over-night charge and performed intermittently.Optimal control has previously been demonstrated for fast-charging in EV applications [1]. However, electrochemical, and mechanical degradation need to be considered to allow optimal fast charging throughout batteries’ lifetimes. Khalik et al. [5] demonstrated how a charging procedure could be designed a-priori for the entire cycle life, considering a direct implementation of the solid-electrolyte-interphase growth and capacity fade due to the associated lithium consumption. Such approaches can extend battery lifetime, but neither yield optimal charging-procedures at every state-of-health nor handle unknown aging modes on the fly. Our proposed approach involves intermittent identification of electrochemical model parameters and subsequent update of the fast-charging protocol such that it adheres to electrochemical constraints throughout the entire battery lifetime.In this work, summarized in Figure 1, we present this methodology and show results from an experimental study testing the proposed approach vs. conventional charging procedures. The experimental study is performed on state-of-the art automotive cells used in heavy-duty electric vehicles.Figure caption: Comparison of a conventional electrochemically constrained fast-charging procedure with the aging-sensitive approach proposed in the present work.

Estimation of internal states in a Li-ion battery using BiLSTM with Bayesian hyperparameter optimization

An accurate state estimator is critical to Battery Management Systems (BMSs). In more advanced BMSs, state estimators based on electrochemical-thermal battery models are preferred because they can predict the internal states of the battery. However, these physics-based models are computationally costly and time-demanding, preventing them from being directly used for real-time estimation. As an alternative approach, data-driven models, like Deep Neural Networks (DNNs), can be trained on datasets generated by electrochemical models. Increasing the accuracy of data-driven models can be achieved by applying more complex architectures of DNN. However, such DNNs are computationally time-demanding, which makes them inefficient for online state estimation algorithms. To address this issue, we have proposed a BiLSTM model as a state estimator whose hyperparameters are automatically optimized using a Bayesian Optimization (BO) framework. We demonstrate that by applying Bayesian inference, one can have a highly accurate state estimator while using a less complex DNN architecture which allows for being computationally efficient as well. The high accuracy of the BiLSTM model with the optimized combination of hyperparameters is demonstrated by the low Root Mean Squared Errors (RMSE) levels on the test data set.

Modeling of the Battery Pack and Battery Management System towards an Integrated Electric Vehicle Application

The transportation sector is under increasing pressure to reduce greenhouse gas emissions by decarbonizing its operations. One prominent solution that has emerged is the adoption of electric vehicles (EVs). As the electric vehicles market experiences rapid growth, the utilization of lithium-ion batteries (LiB) has become the predominant choice for energy storage. However, it is important to note that lithium-ion battery technology is sensitive to factors, like excessive voltage and temperature. Therefore, the development of an accurate battery model and a reliable state of charge (SOC) estimator is crucial to safeguard against the overcharging and over-discharging of the battery. Numerous studies have been conducted to address lithium-ion battery cell modeling and SOC estimations. These studies have explored variations in the number of RC networks within the model and different estimation methods. However, it is worth mentioning that the capacity of a single lithium-ion battery cell is relatively low and cannot be directly employed in electric vehicles. To meet the total capacity and voltage requirements for electric vehicles, multiple cells are typically connected in series or parallel configurations to form a battery pack. Surprisingly, this aspect has often been overlooked in previous research. To tackle this overlooked challenge, our study introduces a comprehensive battery pack model and an advanced Battery Management System (BMS). We then integrate these components into an electric vehicle model. Subsequently, we simulate the integrated EV-BMS model under the conditions of four different urban driving scenarios to replicate real-world driving conditions. The BMS that we have developed includes an Extended Kalman Filter (EKF)-based SOC estimation system, a mechanism for controlling coolant flow, and a passive cell-balancing algorithm. These components work together to ensure the safe and efficient operation of the battery pack within the electric vehicles.

System identification and state estimation of a reduced-order electrochemical model for lithium-ion batteries

Lithium-ion batteries commonly used in electric vehicles are an indispensable part of the development process of decarbonization, electrification, and intelligence in transportation. From intelligent designing, manufacturing to controlling, an intelligent battery management system plays a crucial role in the long life, high efficiency, and safe operation of lithium-ion batteries. As a first-principle model, the electrochemical parameters of the electrochemical model have physical meanings and reflect the internal state of the lithium-ion batteries. The application of electrochemical models in an advanced intelligent battery management system is a future trend that promises to mitigate battery life degradation and prevent safety incidents. The reduced-order electrochemical model is expected to alleviate the requirements of advanced battery management systems for high accuracy and fast computing of lithium-ion battery models. However, the existing model order reduction methods have the drawbacks of high computational complexity and small application scope, so that inconvenient to apply onboard. In order to solve the existing obstacles, this paper applies the pseudo-spectral method to solve the solid-phase diffusion equation, while the liquid-phase concentration equation is simplified by the Galerkin method. Subsequently, a particle swarm optimization algorithm is used to identify 11 parameters of the electrochemical model. To further improve the accuracy of the electrochemical model, the above system identification method is applied to segment identification, especially for high or low state-of-charge (SoC) conditions in this study. Finally, based upon the derived model, estimation of SoC is performed using a particle filter. The results show that the proposed reduced-order electrochemical model achieves a low Mean Absolute Error (MAE) of 8.4 mV and a MAE of 0.54 % on estimation of SoC based on the envisaged particle filter. This work is expected to provide the basis for the subsequent development of lithium-ion battery electrochemical models with smaller identification parameters and faster identification processes.

Data selection framework for battery state of health related parameter estimation under system uncertainties

Data selection is a practical technique for improving parameter estimation accuracy through the strategic selection of information-rich data for use in the estimation algorithm. Traditional selection criteria have been either heuristic or sensitivity-based, without consideration of uncertainties in measurement, model, or parameter. In this paper, we propose an uncertainty-aware data selection framework that selects data segments based on the potential of the ingrained data structures to mitigate the influence of system uncertainties on the estimation result. The framework comprises two components: the data quality rating and data selection algorithm. The data quality rating is a metric for evaluating the uncertainty-propagating data structures of a data segment, and the data selection algorithm automatically integrates the data selection into the estimation procedure. Furthermore, a novel adaptive approximation of model/measurement uncertainty is derived and implemented in the data quality rating formula to enhance performance in the presence of time-varying sensor bias/noise and unmodeled system dynamics. The framework is validated through an advanced battery management system application, where two lithium-ion battery health-related electrochemical parameters are separately estimated under random drive-cycle input data to emulate battery state of health monitoring for an electric vehicle. We show that the drive-cycle data, which are frequently used for battery state of health estimation as the only available data during battery operation, may not provide accurate estimation results due to the existence of large portions of low-quality data (low sensitivity and high uncertainty). By extracting the high-quality data segments, the data selection framework reduced experimental estimation errors by one order of magnitude when compared with the conventional approach of estimating without data selection.

Improving the convergence rate of Newman’s battery model using 2nd order finite element method

While battery cycling experiments last for years, battery modelling can save time and is environment friendly, to study the degradation mechanisms of lithium-ion batteries. However, battery models are being challenged by issues of long-time calculation, poor accuracy and bad convergence. In this work, a Julia-based framework (Jubat) has been developed for Newman’s battery model. This framework benefits from the high execution efficiency of Julia language, so the mean computational time is lower than its counterpart PyBaMM written in Python. Governing equations are solved by the finite element method, and good accuracy is obtained. The convergence rate is improved by 2nd order elements, which capture better flux variation within elements than the finite volume method and the finite element method using linear elements, with slightly higher computation cost. Numerical examples show easy use of this framework and ability to solve practical problems like drive cycles. This work provides a compact, readable and fast framework for battery modelling and is useful for the rapid development of advanced battery management system. The code is open-source at https://github.com/weilongai/JuBat.

Smart energy storage systems review on determining efficient battery management systems

Energy storage systems have become a crucial component of smart grids, enabling the efficient integration of renewable energy sources, and reducing peak demand. Smart energy storage systems (SESS) are considered one of the key technologies for the future power system, and battery management systems (BMS) play a critical role in optimizing their performance. This paper provides a comprehensive review of SESS and BMS, focusing on the determination of efficient battery management systems. First, the paper presents an overview of SESS and their importance in modern power systems. The different types of SESS, including electrochemical, thermal, and mechanical energy storage systems, are discussed along with their advantages and limitations. Then, the paper presents a detailed description of the BMS and its functions, such as state-of-charge estimation, cell balancing, and thermal management. Next, the paper reviews the recent developments in BMS design and implementation, including hardware and software advancements. The importance of accurate modeling and simulation of battery systems is also discussed, as it can help optimize the performance and extend the lifetime of batteries. The paper also highlights the challenges associated with BMS, such as safety concerns, and discusses potential solutions. Finally, the paper concludes with a summary of the current state of the art in SESS and BMS and provides recommendations for future research. These include the need for further development of advanced BMS technologies, including artificial intelligence and machine learning, to improve battery performance, safety, and reliability. Additionally, the paper suggests the importance of standardization and interoperability of BMS systems to enable seamless integration into the grid. Overall, this paper provides a valuable resource for researchers and practitioners interested in SESS and BMS design and optimization.