Battery Design and Management

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape. Full article

In this study, an energy management model for electric vehicles including the entire vehicle such as the cabin, electric motors, battery, and the heating–cooling system was prepared. The heating and cooling processes for electric vehicles were run according to the internationally recognized driving cycles as well as at constant speeds to investigate them under different ambient conditions. The heating–cooling processes were managed in line with the cabin temperature target determined by considering the comfort conditions. The energy consumption of each of the system elements and the system in the heating–cooling process in electric vehicles was analyzed. Under different operating conditions, the variation of cabin temperature with time, instantaneous power, and cumulative energy consumption was calculated. The effect of heating and cooling processes on energy consumption, charging rate, and range were analyzed and interpreted. The results showed that the heating–cooling system for the heating process consumed more energy when the ambient temperature decreased, and the charge consumption ratio as well as the range deformation rate increased to about 30% when the ambient temperature was –10 °C. Similarly, the heating–cooling system for the cooling process consumed more energy when the ambient temperature increased, and the charge consumption ratio as well as the range deformation rate reached up to 40% when the ambient temperature was 40 °C. When the outdoor conditions were close to the thermal comfort temperature of 23 °C inside the cabin, the total energy consumption and the range deformation rates were reduced to less than 10%. Full article

In modern electric vehicle applications, understanding the evolution of the internal electrochemical reaction throughout the aging of batteries is as relevant as knowing their state of health. This article demonstrates the feasibility of correlating a mechanistic model of the battery internal electrochemical reactions with an equivalent electrical circuit (EEC) model, providing a practical and understandable interpretation of the internal reactions for electrical specialists. By way of electrochemical impedance spectroscopy analysis and automatic control theory, a methodology for correlating the resistance and capacitance variations of the EEC model and how they reflect the electrochemical reaction changes is proposed. These changes are represented through the time constants of the three $RC$ parallel arrays from an EEC model. PS-260 lead–acid batteries were analyzed throughout the SOC and their useful life to validate this methodology. The result analysis allows us to establish that the first $RC$ array corresponds to the negative electrode reactions in the range of 1.48 Hz to 10 kHz, the second $RC$ array to the positive electrode reactions and generation of sulfates in the range of 0.5 to 1.48 Hz, and the third $RC$ array to the generation of sulfates and their diffusion in the range of 0.01 to 0.5 Hz. Full article

Partial state of charge (PSOC) is an important use case for lead–acid batteries. Charging times in lead–acid cells and batteries can be variable, and when used in PSOC operation, the manufacturer’s recommended charge times for single-cycle use are not necessarily applicable. Knowing how long charging will take and what the variability in time required is allows for better planning of operations and algorithm creation for battery energy storage system (BESS) manufacturers. This paper details and demonstrates a procedure for identifying the charging time of cells when different charge throughputs occur prior to reaching full charge. The results showed that the charging time in PSOC operations was highly variable when a charge-factor-controlled full-charge procedure was used. Also noted were that higher voltages for the same state of charge were reached as the number of cycles following reaching full charge increased. None of the regimes tested in this paper caused any significant capacity degradation, which demonstrates that PSOC operations can be performed even on cells not specifically designed for them, provided the correct regime is chosen. Full article

The performance of lithium-ion batteries is greatly influenced by various factors within their operating environment, which can significantly impact their overall efficiency and effectiveness. In this paper, a multi-physics field electrochemical thermal model is established to measure the physical parameters of a battery module during the charge/discharge process. The effects of working temperature, current rate, and convective heat transfer coefficient are investigated by establishing an electrochemical and thermal model. The results are obtained by conducting numerous parameterized scans to analyze the system’s state across various operating conditions, enabling the determination of its temperature and the selection of appropriate cooling measures accordingly. Based on the internal and external conditions of battery operation, parameter selection corresponding to the operating range is divided into several stages, with thermal management strategies provided for each stage. The existing framework facilitates the design of battery packs equipped with efficient thermal management strategies, thereby enhancing the battery systems’ reliability and performance. Furthermore, it aids in establishing optimal operational and safety boundaries for batteries. Full article

The significant amount of heat generated during the discharge process of a lithium-ion battery can lead to battery overheat, potential damage, and even fire hazards. The optimal operating temperature of a battery ranges from 25 °C to 45 °C. Hence, battery thermal management cooling techniques are crucial for controlling battery temperature. In this work, the cooling of 21700 lithium-ion batteries during their discharging processes using phase-change materials (PCMs) and porous pure copper foams were simulated. The effects of discharge intensities, battery orientations, and battery arrangements were investigated by observing the changes in temperature distributions. Based on current simulations for a 2C discharge, air-cooled vertical batteries arranged in unidirectional configuration exhibit an increase in heat dissipation by 44% in comparison to the horizontal batteries. This leads to a decrease in the maximum battery temperature by about 10 °C. The use of either PCMs or copper foams can effectively cool the batteries. Regardless of the battery orientation, the maximum battery temperature during a 2C discharge drops dramatically from approximately 90 °C when air-cooled to roughly 40 °C when the air is replaced by PCM cooling or when inserted with a copper foam of 0.9 porosity. If the PCM/copper foam approach is implemented, this maximum temperature further decreases to slightly above 30 °C. Although not very significant, it has been discovered that crossover arrangement slightly reduces the maximum temperature by no more than 1 °C. When a pure copper foam with a porosity ranging from 0.90 to 0.97 is saturated with a PCM, the excellent thermal conductivity of pure copper, combined with the PCM latent heat absorption, can best help maintain the battery pack within its range of optimal operating temperatures. If the porosity of the copper foam decreases from 0.95 to 0.5, the volumetric average temperature of the batteries may increase from 30 °C to 31 °C. Full article

Accurate and real-time estimation of pack system-level chips is essential for the performance and reliability of future electric vehicles. Firstly, this study constructed a model of a nickel manganese cobalt cell on the ground of the electrochemical process of the packs. Then, it used methods on the grounds of the unscented Kalman filter and unscented Kalman particle filter for system-level chip estimation and algorithm construction. Both algorithms are on the ground of Kalman filters and can handle nonlinear and uncertain system states. In comparative testing, it can be seen that the unscented Kalman filter algorithm can accurately evaluate the system-level chip of the nickel manganese cobalt cell under intermittent discharge conditions. The system-level chip was 0.53 at 1000 s and was reduced to 0.45 at 1500 s. These results demonstrate that the evaluation of the ternary lithium battery pack’s performance is time-dependent and indicate the accuracy of the algorithm used during this time period. These data should be considered in the broader context of the study for a comprehensive understanding of their meaning. In the later stage, the estimation error of the recursive least-squares unscented Kalman particle filter method for system-level chips began to significantly increase, gradually exceeding 1%, with a corresponding root-mean-square error of 0.002171. This indicates that the recursive least-squares optimization algorithm, the unscented Kalman particle filter algorithm, diminished its root mean square error by 27.59%. The unscented Kalman filter and unscented Kalman particle filter are effective in estimating the system-level chip of nickel manganese cobalt cells. However, UPF performs more robustly in handling complex situations, such as pack aging and temperature changes. This study provides a new perspective and method that has a high reference value for pack management systems. This helps to achieve more effective energy management and improve pack life, thereby enhancing the reliability and practicality of electric vehicles. Full article

Magnesium-dissolved oxygen seawater batteries have open structures and flow seawater as electrolytes. These two features attract much attention. The cathode electrode is one of the key components that affect the performance of seawater batteries. In this study, seawater batteries with carbon cathodes made from three commercial carbons were investigated and discussed. The porous structure of the cathode was adjusted by changing the mass ratio between polytetrafluoroethylene (PTFE) and carbon materials. The binder ratios range from 10% to 50%. The structure of the different porous carbon cathodes was characterized, and the discharging performance was analyzed. Results showed that the number of pores with diameters of 2–10 nm decreased as the PTFE ratio increased. Meanwhile, as the PTFE ratio increased from 10% to 50%, the seawater battery discharging voltage and capacity were first inhibited when the PTFE ratio was less than 20% but then promoted. It revealed that a balance should be achieved between the number of reaction sites and the paths for oxygen transfer. Moreover, the oxygen transfer in the porous electrode is more important for batteries working in seawater. This study practically investigates seawater batteries with various PTFE binder ratios and provides a reference for the design of magnesium-dissolved oxygen seawater batteries. Full article

The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size ($\mathrm{M}$) greatly affects the accuracy of SoC estimation in both methods. $\mathrm{M}$ provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC. Full article

Higher-energy-density, Wh L⁻¹ or Wh kg⁻¹, lithium-ion cells are one of the critical advancements required for the implementation of electric vehicles. This increase leads to a longer drive distance between recharges. Aside from material development, full lithium-ion cell design parameters have the potential to greatly influence fabricated cell energy density. The following work highlights the impact of these full-cell design parameters, investigating the effect of a negative to positive capacity ratio, positive electrode porosity, positive electrode active material content, and overall charge voltage on stack volumetric energy density. Decreasing the N:P ratio or increasing active material content results in an almost identical volumetric energy density increase: ~4%. Decreasing the positive electrode porosity from 40–30% or increasing the charge voltage from 4.2–4.35 V also results in an almost identical increase in volumetric energy density: ~5.5%. Combining all design changes has the potential to increase stack volumetric energy density by 20% compared to the baseline cell design. Full article

Accurate state of charge (SOC) estimation is helpful for battery management systems to extend batteries’ lifespan and ensure the safety of batteries. However, due to the pseudo-positive definiteness of the covariance matrix and noise statistics error accumulation, the SOC estimation of lithium-ion batteries is usually inaccurate or even divergent using Kalman filters, such as the unscented Kalman filter (UKF) and the square-root unscented Kalman filter (SRUKF). To resolve this problem, an SOC estimation method based on the dual-coefficient tracking improved square-root unscented Kalman filter for lithium-ion batteries is developed. The method is composed of an improved square-root unscented Kalman filter (ISRUKF) and a dual-coefficient tracker. To avoid the divergence of SOC estimation due to the covariance matrix with pseudo-positive definiteness, an ISRUKF based on the QR decomposition covariance square-root matrix is presented. Moreover, the dual-coefficient tracker is designed to track and correct the state noise error of the battery, which can reduce the SOC estimation error caused by the accumulation of the battery model error using the ISRUKF. The accuracy and robustness of the SOC estimation method using the developed method are validated by the comparison with the UKF and SRUKF. The developed algorithm shows the highest SOC estimation accuracy with the SOC error within 1.5%. Full article

Power battery scheduling optimization can improve the service life of the battery, but the existing heuristic algorithm has poor adaptability, and the capacity fluctuates significantly in the cycle aging process, which makes it easy to fall into the local optimal. To overcome these problems, we take the battery cycle life maximization as the goal, propose a reinforcement learning scheduling optimization model with temperature and internal resistance difference constraints, so as to determine whether to charge or discharge during battery cycle aging. We do this using the deep−learning−based battery capacity estimation model as the learning environment for the agent, using the Double DQN algorithm to train the agent, and proposing the principal component analysis method to reduce the dimension of the state space. These experiments, using multiple publicly available battery aging data sets, show that the principal component analysis method and the constraint functions reduce the computational time to find the optimal solution, providing the possibility of obtaining larger reward values. Meanwhile, the trained model effectively extends the cycle life of the battery, and has good adaptivity. It can automatically adjust parameters with the battery aging process to develop optimal charging and discharging protocols for power batteries with different chemical compositions. Full article

The pace of the implementation of renewable electricity storage in Europe is disappointingly slow. Several factors influence this and there is a need to speed up the rate and increase the volumes in order to promote a 100% transition to renewable energy resources, expand the practice of using renewable energy, and contribute to the improvement of the quality of life of consumers. An important factor is significantly reducing impact on the environment and climate change. Electricity from renewable energy sources such as solar and wind has a seasonal nature that cannot provide the necessary electricity consumption and cover peak loads. The so-called “energy resource crisis” is also a very topical problem at the moment, which reinforces the global need to increase the share of renewable energy resources in the overall balance of primary energy resources. Practical wider integration of renewable electricity storage is what can help stimulate this. The availability of renewable electricity is constantly increasing, and the level of technological innovation is rapidly developing. Therefore, it is valuable to analyse, look for connections and for ways to accumulate electricity in order to promote its availability from private homes to the national scale and more broadly on the European scale. Therefore, this article analyses and compares the different options for renewable electricity storage, from small batteries to large storage systems, arriving at the best solution according to needs, using analysis methods such as multicriteria decision analysis (MCDA) and TOPSIS. After comparing nine criteria, such as the amount of investment required, existing power density, efficiency, duration of operation, and others in two groups (small and large accumulation systems), it was concluded that lithium-ion batteries are currently the best solution among batteries, while pumped hydro storage is the best solution among large accumulation systems. Full article

A rapid growth in electric vehicles has led to a massive number of retired batteries in the transportation sector after 8–10 years of use. However, retired batteries retain over 60% of their original capacity and can be employed in less demanding electric vehicles or stationary energy storage systems. As a result, the management of end-of-life electric vehicles has received increased attention globally over the last decade due to their environmental and economic benefits. This work presents knowledge and technology for retired electric vehicle batteries that are applicable to the Thai context, with a particular focus on a case study of a retired lithium-ion battery from the Nissan X-Trail Hybrid car. The disassembled battery modules are designed for remanufacturing in small electric vehicles and repurposing in energy storage systems. The retired batteries were tested in a laboratory under high C-rate conditions (10C, 20C, and 30C) to examine the limitations of the batteries’ ability to deliver high current to electric vehicles during the driving operation. In addition, the electric motorcycle conversion has also been studied by converting the gasoline engine to an electric battery system. Finally, the prototypes were tested both in the laboratory and in real-world use. The findings of this study will serve as a guideline for the sorting and assessment of retired lithium-ion batteries from electric vehicles, as well as demonstrate the technical feasibility of reusing retired batteries in Thailand. Full article

The bottleneck in the implementation of hybrid lithium-sodium-ion batteries is the lack of anode materials with a desired rate capability. Herein, we provide an in-depth examination of the Li-storage performance of sodium titanate nanowires as negative electrodes in hybrid Li,Na-ion batteries. Titanate nanowires were prepared by a simple and reproducible hydrothermal method. At a low reaction pressure, the well-isolated nanowires are formed, while by increasing the reaction pressure from 2 to 30 bar, the isolated nanowires tend to bundle. In nanowires, the local coordinations of Na and Ti atoms deviate from those in Na₂Ti₃O₇ and Na₂Ti₆O₁₃ and slightly depend on the reaction pressure. During the annealing at 350 °C, both Na and Ti coordinations undergo further changes. The nanowires are highly defective, and they easily crystallize into Na₂Ti₆O₁₃ and Na₂Ti₃O₇ phases. The lithium storage properties are evaluated in lithium-ion cells vs. lithium metal anode and titanate electrodes fabricated with PVDF and carboxymethyl cellulose (CMC) binders. The Li-storage by nanowires proceeds by a hybrid capacitive-diffusive mechanism between 0.1 and 2.5 V, which enables to achieve a high specific capacity. Sodium titanates accommodate Li⁺ by formation of mixed lithium-sodium-phase Na_2−xLi_xTi₆O₁₃, which is decomposed to the distinct lithium phases Li_0.54Ti_2.86O₆ and Li_0.5TiO₂. Contrary to lithium, the sodium storage is accomplished mainly by the capacitive reactions, and thus the phase composition is preserved during cycling in sodium ion cells. The isolated nanowires outperform bundled nanowires with respect to rate capability. Full article

The reasonable allocation of the battery energy storage system (BESS) in the distribution networks is an effective method that contributes to the renewable energy sources (RESs) connected to the power grid. However, the site and capacity of BESS optimized by the traditional genetic algorithm is usually inaccurate. In this paper, a power grid node load, which includes the daily load of wind power and solar energy, was studied. Aiming to minimize the average daily distribution networks loss with the power grid node load connected with RESs, a site selection and capacity setting model of BESS was built. To solve this model, a modified simulated annealing genetic algorithm was developed. In the developed method, the crossover probability and the mutation probability were modified by a double-threshold mutation probability control, which helped this genetic method to avoid trapping in local optima. Moreover, the cooling mechanism of simulated annealing method was presented to accelerate the convergence speed of the improved genetic algorithm. The simulation results showed that the convergence speed using the developed method can be accelerated in different number BESSs and the convergence time was shortened into 35 iteration times in view of networks loss, which reduced the convergence time by about 30 percent. Finally, the required number of battery system in BESS was further built according to the real batteries grouping design and the required capacity of BESS attained using the developed method. Full article

The heat dissipation characteristics of the lithium-ion battery pack will have an effect on the overall performance of electric vehicles. To investigate the effects of the structural cooling system parameters on the heat dissipation properties, the electrochemical thermal coupling model of the lithium-ion power battery has been established, and the discharge experiment of the single battery has been designed. The voltage and temperature curves with time are similar to those obtained from the numerical model at various discharge rates, and the experimental results are relatively accurate. Based on this model, the height, angle, and number of different air inlets and outlets are designed, and the heat dissipation characteristics of different structural parameters are analyzed. The results show that the maximum temperature decreases by 3.9 K when the angle increases from 0° to 6°, the average temperature decreases by 2 K and the maximum temperature difference decreases by 2.9 K when the height increases from 12 mm to 16 mm, and the more the number of air inlets and outlets there are, the better the heat dissipation effect is. Therefore, the air vent of the battery cooling system has an important impact on the heat dissipation characteristics of the battery, which should be fully considered in the design. Full article

The deployment of battery-powered electric vehicles in the market has created a naturally increasing need for the safe deactivation and recycling of batteries. Various deactivating methods for lithium-ion cells include electrical discharging or deactivation with liquids. Such methods are also useful for cases where the cell tabs are not accessible. In the literature analyses, different deactivation media are used, but none include the use of calcium chloride (CaCl₂) salt. As compared to other media, the major advantage of this salt is that it can capture the highly reactive and hazardous molecules of Hydrofluoric acid. To analyse the actual performance of this salt in terms of practicability and safety, this experimental research aims to compare it against regular Tap Water and Demineralized Water. This will be accomplished by performing nail penetration tests on deactivated cells and comparing their residual energy against each other. Moreover, these three different media and respective cells are analysed after deactivation, i.e., based on conductivity measurements, cell mass, flame photometry, fluoride content, computer tomography and pH value. It was found that the cells deactivated in the CaCl₂ solution did not show any signs of Fluoride ions, whereas cells deactivated in TW showed the emergence of Fluoride ions in the 10th week of the insertion. However, with the addition of CaCl₂ in TW, the deactivation process > 48 h for TW declines to 0.5–2 h, which could be an optimal solution for real-world situations where deactivating cells at a high pace is essential. Full article

There is increased talk about using second-life batteries in applications. In first-life applications, the batteries start from new, and a range of life cycle estimation techniques are applied. However, it is not clear how second-life batteries should be monitored compared to first life batteries. This paper investigated different algorithms from first-life applications for estimating and forecasting battery cell state of health in conjunction with capacity calculations using second life cells under long term durability testing. The paper looks at how close these models predict capacity fade based on a set of second-life batteries that have been undertaking sweat testing over six different applications. The paper concludes that there are two methods that could be suitable candidates for predicting lifespan. One of these needed to be modified from the original. Full article

Fast charging is a key requirement for customer acceptance of battery electric vehicles. Fast charging of lithium-ion batteries is limited by lithium plating, an undesired side reaction that leads to rapid degradation and poses a potential safety hazard. In order to approach but not exceed the lithium plating current limit during fast charging, a variety of analytical tools have been developed to detect lithium plating. In this publication, we propose a new impedance-based method for the time-resolved detection of lithium plating. The proposed method was demonstrated with an integrated cell monitoring circuit capable of measuring the impedance during cell operation, bringing the feasibility of implementation in an automotive target application within reach. Importantly, the proposed method eliminates the temperature dependence which is an intrinsic problem for impedance-based lithium plating detection in automotive lithium-ion cells, thus making on-board plating detection feasible. Full article

One of the major challenges of battery cell manufacturing is the reduction of production costs. Production defects and manufacturing inaccuracies, combined with high value streams, cause cost-intensive scrap rates. Conventional batch tracing is insufficient to detect rejects at an early stage, since the quality-critical intermediate products are not considered in a differentiated manner. To address this deficiency, tracking and tracing approaches in battery cell production are becoming increasingly popular. To obtain sufficient resolutions of the production data, the allocation of process and product data must be performed at the electrode sheet level. An interface is required for this, which can be realized by marking the individual electrodes. This paper investigates the integration of two well-known marking technologies: laser and ink marking. Integrating these marking technologies requires the consideration of physical boundary conditions in the process chain. For this purpose, the necessary investigations are presented in a structured manner to ensure that the marking does not have a negative influence on the process chain and vice versa. A pilot production line is used as an example to demonstrate the necessary tests for the integration of laser or ink markings. Full article

The recycling of spent lithium-ion batteries (Li-ion Batteries) has drawn a lot of interest in recent years in response to the rising demand for the corresponding high-value metals and materials and the mounting concern emanating from the detrimental environmental effects imposed by the conventional disposal of solid battery waste. Numerous studies have been conducted on the topic of recycling used Li-ion batteries to produce either battery materials or specific chemical, metal or metal-based compounds. Physical pre-treatment is typically used to separate waste materials into various streams, facilitating the effective recovery of components in subsequent processing. In order to further prepare the recovered materials or compounds by applying the principles of materials chemistry and engineering, a metallurgical process is then utilized to extract and isolate pure metals or separate contaminants from a particular waste stream. In this review, the current state of spent Li-ion battery recycling is outlined, reviewed, and analyzed in the context of the entire recycling process, with a particular emphasis on hydrometallurgy; however, electrometallurgy and pyrometallurgy are also comprehensively reviewed. In addition to the comprehensive review of various hydrometallurgical processes, including alkaline leaching, acidic leaching, solvent (liquid-liquid) extraction, and chemical precipitation, a critical analysis of the current obstacles to process optimization during Li-ion battery recycling is also conducted. Moreover, the energy-intensive nature of discussed recycling process routes is also assessed and addressed. This study is anticipated to offer recommendations for enhancing wasted Li-ion battery recycling, and the field can be further explored for commercialization. Full article

Proton exchange membrane fuel cells (PEMFC) are vulnerable to oxygen starvation when working under variable load. To address these issues, a cascade control strategy of sliding mode control (SMC) and Proportion Integration Differentiation (PID) control is proposed in this study. The goal of the control strategy is to enhance the PEMFC’s net power by adjusting the oxygen excess ratio (OER) to the reference value in the occurrence of a load change. In order to estimate the cathode pressure and reconstruct the OER, an expansion state observer (ESO) is developed. The study found that there is a maximum error of about 2200Pa between the estimated cathode pressure and the actual pressure. Then the tracking of the actual OER to the reference OER is realized by the SMC and PID cascade control. The simulation study, which compared the control performance of several methods—including PID controller, adaptive fuzzy PID controller and the proposed controller, i.e., the SMC and PID cascade controller—was carried out under various load-changing scenarios. The outcomes demonstrate that the proposed SMC and PID cascade controller method really does have a faster response time. The overshoot is reduced by approximately 3.4% compared to PID control and by about 0.09% compared to fuzzy adaptive PID. SMC and PID cascade control reference OER performs more effectively in terms of tracking compared to PID control and adaptive fuzzy PID control. Full article

State of charge (SOC) is one of the most important parameters in battery management systems, and the accurate and stable estimation of battery SOC for real-world electric vehicles remains a great challenge. This paper proposes a long short-term memory network based on grid search and cross-validation optimisation to estimate the SOC of real-world battery systems. The real-world electric vehicle data are divided into parking charging, travel charging, and finish charging cases. Meanwhile, the parameters associated with the SOC estimation under each operating condition are extracted by the Pearson correlation analysis. Moreover, the hyperparameters of the long short-term memory network are optimised by grid search and cross-validation to improve the accuracy of the model estimation. Moreover, the gaussian noise algorithm is used for data augmentation to improve the accuracy and robustness of SOC estimation under the working conditions of the small dataset. The results indicate that the absolute error of SOC estimation is within 4% for the small dataset and within 2% for the large dataset. More importantly, the robustness and effectiveness of the proposed method are validated based on operational data from three different real-world electric vehicles, and the mean square error of SOC estimation does not exceed 0.006. This paper aims to provide guidance for the SOC estimation of real-world electric vehicles. Full article

As a power source for autonomous underwater vehicles (AUVs), lithium-ion batteries play an important role in ensuring AUVs’ electric power propulsion performance. An accurate state of charge (SOC) estimation method is the key to achieving energy optimization for lithium-ion batteries. Due to the complicated ocean environments, traditional filtering methods cannot effectively estimate the SOC of lithium-ion batteries in an AUV. Based on the standard extended Kalman filter (EKF), an adaptive iterative extended Kalman filter (AIEKF) method for the SOC in an AUV is proposed to address the traditional filter’s problems, such as low accuracy and large errors. In this method, the adaptive update is introduced to deal with the uncertain noise from the lithium-ion battery. The iteration is used to improve the convergence speed and to reduce the computational burden. Compared with the EKF, iterative extended Kalman filter (IEKF) and adaptive extended Kalman filter (AEKF), the proposed AIEKF has a higher estimation accuracy and anti-interference capability, which is suitable for the AUV’s SOC estimation. In addition, based on the second-order equivalent circuit model of the lithium-ion battery, a forgetting factor recursive least squares (FFRLS) method is proposed to deal with the multi-variability problem. In the end, four different methods, including EKF, IEKF, AEKF, and the proposed AIEKF, are compared in computational time. The experiment results show that the proposed method has high accuracy and fast estimation speed, meaning that it has good application potential in AUVs. Full article

Input voltage, current, and temperature measurement circuits are the vital concerns of a Battery Management System (BMS) in electric vehicles. There are several approaches proposed to analyze the parameters of voltage, current, and temperature of a battery. This paper proposes a BMS methodology that is designed using linear optocouplers. In this paper, the optocouplers are incorporated between the battery pack and the BMS, which can be used in automotive applications for accurate measurements. The functions of BMS, such as measuring the current, voltage, and temperature in real time, can be executed using the proposed methodology. Full article

The large volume change of Si has been a roadblock in deploying high-capacity Si-based electrodes in lithium-ion batteries, causing salient structural changes and prominent chemo-mechanical coupled degradation. However, the effects of the volume change of Si-based active materials on the structural parameters have not been fully understood, especially for theoretical prediction through fundamental parameters. In this work, we develop a real-time porosity model featuring volume changes of active materials and electrode dimensions for Si-based anodes, predicting the evolution of porosity and electrode dimensions well through the use of basic electrode parameters. The allowable design space of mass fractions of Si is predicted to be lower than 6% for initial porosity in the range of 26–60% based on the permitted limits of maximum volume change of electrode dimensions and minimum porosity at full lithiation. Subsequently, the effects of changes in porosity and electrode dimensions on the gravimetric and volumetric capacities are emphasized, showing that the accurate estimation of electrochemical performance calls more attention to the effects of structural parameters for Si-based anodes. This study provides a simple and practicable method for the design of electrode parameters, and sheds light on the estimation of electrochemical performance for Si-based anodes. Full article

This study aimed to achieve a clear understanding of the response characteristics of soft pack battery extrusion conditions under various situations. In this study, we chose a LiCoO₂ battery as the research object of the extrusion experiment. First, the repeatability of the extrusion test on the battery was verified. A quasi-static extrusion test was conducted on three groups of batteries in the same state, and the load-displacement curves of the three groups of experimental batteries were almost the same. Then, the influence of the extrusion speed on the battery thermal runaway was studied. The results show that a different extrusion speed has a certain impact on the thermal runaway performance of the battery. The peak load of the battery is lower at a lower speed. Finally, the study found that every 20% change in SOC has a greater impact on the battery response under a squeeze. The larger the SOC, the more severe the battery thermal runaway. Through an analysis of multiple experimental cases, it is possible to have a deeper understanding of the temperature and voltage characteristics of lithium batteries when a thermal runaway occurs, which provides ideas for monitoring the trend of the thermal runaway of electric vehicles. Full article

Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified. Full article

This paper proposes a robust design approach based on the Design for Six Sigma (DFSS), to promote the robustness of our previous model-free-adaptive-control-based (MFAC-based) energy management strategy (EMS) for the plug-in hybrid electric vehicles (PHEVs) in real-time application. First, the multi-island genetic algorithm (MIGA) is employed for a deterministic design of the MFAC-based EMS, and the Monte Carlo simulation (MCS) is utilized to evaluate the sigma level of the strategy with the deterministic design results. Second, a DFSS framework is formulated to reinforce the robustness of the MFAC-based EMS, in which the velocity and the vehicle mass are considered external disturbances whilst the terminal state of charge (SOC) of the battery and the fuel consumption (FC) are conducted as responses. In addition, real-time SOC constraints are incorporated into Pontryagin’s minimum principle (PMP) to confine the fluctuation of battery SOC in MFAC-based EMS to make it closer to the solution of the dynamic programming (DP). Finally, the effectiveness of the robust design results is assessed by contrasting with other strategies for various combined driving cycles (including velocity, vehicle mass, and road slope). The comparisons demonstrate the remarkable promotion of the robust design in terms of the energy-saving potential and the performance against external disturbance. The average improvement of the FCs can reach up to a considerable 19.66% and 9.79% in contrast to the charge-depleting and charge-sustaining (CD-CS) strategy as well as the deterministic design of MFAC-based EMS. In particular, the energy-saving performance is comparable to DP, where there is only a gap of −1.68%. Full article

State-of-charge (SOC) estimation of lithium-ion batteries (LIBs) is the basis of other state estimations. However, its accuracy can be affected by many factors, such as temperature and ageing. To handle this bottleneck issue, we here propose a joint SOC-SOH estimation method considering the influence of the temperature. It combines the Forgetting Factor Recursive Least Squares (FFRLS) algorithm, Total Least Squares (TLS) algorithm, and Unscented Kalman Filter (UKF) algorithm. First, the FFRLS algorithm is used to identify and update the parameters of the equivalent circuit model in real time under different battery ageing degrees. Then, the TLS algorithm is used to estimate the battery SOH to improve the prior estimation accuracy of SOC. Next, the SOC is calculated by the UKF algorithm, and finally, a more accurate SOH can be obtained according to the UKF-based SOC trajectory. The battery-in-the-loop experiments are utilized to verify the proposed algorithm. For the cases of temperature change up to 35 °C and capacity decay up to 10%, our joint estimator can achieve ultra-low errors, bounded by 2%, respectively, for SOH and SOC. The proposed method paves the way for the advancement of battery use in applications, such as electric vehicles and microgrid applications. Full article

Sodium–metal chloride batteries are suitable alternatives in battery energy storage systems (BESSs), since they are widely known as a type of high-safety battery. To accurately analyze the cathode microstructure of sodium–metal chloride batteries, in this study, we demonstrate the improved tubular-type simple test cell. This improved tubular-type simple test cell was supplemented from the setbacks of our previous test cell, such as a leak, Ni current collector wavering, and sodium wicking. Through testing of the improved test cells, we focus on cathode microstructure analysis, owing to the elimination of the external failure factors mentioned above. The group of improved test cells have a lower capacity gap of 9.5% in the 1st cycle than the capacity gap of previous test cells (37.2%). This result indicates the advancement of reproducibility. Moreover, the improved test cell has a long life of approximately 7200 h by changing the previous test cell structure. In particular, it is expected that this improved tubular simple test cell can advance the research of tubular-type sodium–metal chloride batteries in a small and academic laboratory. Full article

The lithium-ion battery is considered the primary power supply source for electric vehicles due to its high-energy density, long lifespan, and no memory effect. Its performance and safety highly depend on its operating temperature. Therefore, a battery thermal management system is necessary to ensure an electric vehicle (EV)’s performance. Air as a cooling medium is still used in a wide range of thermal management system applications, owing to its low-cost and lightweight. However, the conventional air-based cooling strategy shows an insufficient heat dissipation capacity and usually fails to block the thermal runaway propagation between batteries. Thus, it is of great importance for improving the heat dissipation of an air-based thermal management system. In this paper, three novel schemes (schemes B, C, and D) are introduced successively based on enhancing the heat transfer capacity and safety of a battery pack under a thermal runaway condition. Schemes B and C introduce a hollow spoiler prism and a spoiler prism filled with phase-change material with fins, respectively. The cooling effects of the three schemes are compared using computational fluid dynamics technology. The models of all the schemes are 3D symmetrical structures. In the CFD model, the battery heat-generating sub-model is incorporated through a user-defined function. The results indicate that all three schemes reduce the maximum temperature and the maximum temperature difference in the pack effectively compared with the conventional air cooling system. Scheme D presents the best cooling performance and hinders the propagation of the TR between adjacent batteries under a TR condition. The paper may provide a feasible method for improving the performance of an air-cooled thermal battery management system. Full article

State-of-charge (SOC) is a relative quantity that describes the ratio of the remaining capacity to the present maximum available capacity. Accurate SOC estimation is essential for a battery-management system. In addition to informing the user of the expected usage until the next recharge, it is crucial for improving the utilization efficiency and service life of the battery. This study focuses on applying deep-learning techniques, and specifically convolutional residual networks, to estimate the SOC of lithium-ion batteries. By stacking the values of multiple measurable variables taken at many time instants as the model inputs, the process information for the voltage or current generation, and their interrelations, can be effectively extracted using the proposed convolutional residual blocks, and can simultaneously be exploited to regress for accurate SOCs. The performance of the proposed network model was evaluated using the data obtained from a lithium-ion battery (Panasonic NCR18650PF) under nine different driving schedules at five ambient temperatures. The experimental results demonstrated an average mean absolute error of 1.260%, and an average root-mean-square error of 0.998%. The number of floating-point operations required to complete one SOC estimation was 2.24 × 10⁶. These results indicate the efficacy and performance of the proposed approach. Full article