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Battery safety has attracted considerable attention worldwide due to the rapid development of wearable electronics and the steady increase in the production and use of electric vehicles. As battery failures are often associated with mechanical‐thermal coupled behaviors, protective shielding materials with excellent mechanical robustness and flame‐retardant properties are highly desired to mitigate thermal runaway. However, most of the thermal insulating materials are not strong enough to protect batteries from mechanical abuse, which is one of the most critical scenarios with catastrophic consequences. Here, inspired by wood, we have developed an effective approach to engineer a hierarchical nanocomposite via self‐assembly of calcium silicate hydrate and polyvinyl alcohol polymer chains (referred as CSH wood). The versatile protective material CSH wood demonstrates an unprecedented combination of light weight (0.018 g cm−3), high stiffness (204 MPa in the axial direction), negative Poisson's ratio (−0.15), remarkable toughness (6.67 × 105 J m−3), superior thermal insulation (0.0204 W m−1 K−1 in the radial direction), and excellent fire retardancy (UL94‐V0). When applied as a protective cover or a protective layer within battery packages, the tough CSH wood can resist high‐impact load and block heat diffusion to block or delay the spread of fire, therefore significantly reducing the risk of property damage or bodily injuries caused by battery explosions. This work provides new pathways for fabricating advanced thermal insulating materials with large scalability and demonstrates great potential for the protection of electronic devices. Inspired by wood, we have developed an effective approach to engineer a hierarchical nanocomposite via self‐assembly of calcium silicate hydrate and polyvinyl alcohol polymer chains. The versatile protective material demonstrates an unprecedented combination of light weight (0.018 g cm−3), high stiffness (204 MPa), negative Poisson's ratio (−0.15), remarkable energy dissipation (6.67 × 105 J m−3), superior thermal insulation (0.0204 W m−1 K−1), and excellent fire retardancy (UL94‐V0).

To solve the disadvantages of the low protection grade, high weight, and high cost of the existing locomotive power battery system, this study optimizes the existing scheme and introduces the design concept of two-stage protection. The purpose of the research is to improve the protection level of the battery pack to IP68, to optimize the sheet metal power battery box structure into a more lightweight frame structure, to simplify the cooling mode of the battery pack for natural air cooling, and to improve the battery protection level and maintain the heat exchange capability. In the course of the study, a design scheme with a two-stage protection function is proposed. The numerical model analyzes the self-load, transverse load, longitudinal load, mode, and fatigue, and optimizes the layout of the power tank cell. The optimized box model was physically tested and economically compared. The results show that: (1) The maximum load stress is 128.4 MPa, which is lower than 235 MPa, the ultimate stress of the box material, and the fatigue factor of the frame box structure is 3.75, which is higher than 1.0, and it is not prone to fatigue damage. (2) Under the low-temperature heating condition, the overall temperature rise of the battery pack is 4.3 °C, which is greater than 2.3 °C under the air conditioning heat dissipation scheme. Under the high-temperature charging condition, the overall temperature rise of the battery pack is 2.0 °C, and the temperature value is the same as the temperature rise under the air conditioning cooling scheme. Under the high-temperature discharge condition, the overall temperature rise of the battery pack is 3.0 °C, and the temperature value is greater than 2.1 °C under the air conditioning heat dissipation scheme. At the same time, the temperature rise under the three working conditions is less than the 15 °C stipulated in the JS175-201805 standard. The simulation results show that the natural airflow and two-stage protection structure can provide a good temperature environment for the power battery to work. (3) The optimized box prototype can effectively maintain the structural integrity of the battery cell in the box in extreme test cases, reducing the probability of battery fire caused by battery cell deformation. (4) The power battery adopts a two-stage protection design under the battery power level, which can simultaneously achieve battery protection and prevent thermal runaway, while reducing costs. The research results provide a new concept for the design of a locomotive power battery system. (5) The weight of the optimized scheme is 2020 kg, and the original scheme is 2470 kg; thus, the reduction in weight is 450 kg. Meanwhile, the volume of the optimized scheme is 1.49 m3, and the original scheme is 1.93 m3; thus, the reduction in volume is 0.44 m3.

In 2021, the electric vehicles (EVs) market reached a record-breaking 6.5 million vehicles, and it will continuously grow to USD 31 million in 2030. However, the risk of battery damage should be reduced using a lightweight crashworthy protection system, which can be performed through design optimization to achieve maximum Specific Energy Absorption (SEA). Maximum SEA can be gained by selecting a material with a light weight and high energy absorption properties. An auxetic-shaped cell structure was used since its negative Poisson ratio yields better energy absorption. The research was performed by varying the auxetic cell shape (Re-entrant, Double Arrow, Star-shaped, Double-U), material selection (GFRP, CFRP, aluminum, carbon steel), and geometry variables until the maximum possible SEA was reached. The Finite Element Method (FEM) was used to simulate the impact and obtain the value of the SEA of the varied auxetic cellular structure design samples. The design variation amounted to 100 samples generated using Latin Hypercube Sampling (LHS) to distribute the variables. Finally, the Machine Learning method predicted the design that yielded maximum SEA. The optimization process through Machine Learning consisted of two processes: model approximation using an Artificial Neural Network (ANN) and variable optimization using a Nondominated Sorting Genetic Algorithm-II (NSGA-II). The optimization demonstrated that the maximum SEA resulted from Star-shaped auxetic cells and aluminum material with a thickness of 2.95 mm. This design yielded 1220% higher SEA compared to the baseline model. A numerical simulation was also carried out to validate the result. The prediction error amounted to 6.7%, meaning that the approximation model can successfully predict the most optimum design. After the complete battery system configuration simulation, the design could also prevent excessive battery deformation. Therefore, the optimized structure can protect the battery from failure.

Nowadays, thermal runaway accidents involving lithium batteries in new energy vehicles and energy storage power stations occur frequently, with battery deformation pressure as the core precursor signal. Traditional battery protection schemes suffer from limitations, including wired connections, limited real-time remote monitoring, and insufficient sensing accuracy, rendering them unable to meet the safety monitoring needs of large-scale battery modules. Therefore, a high-precision pressure-sensing battery protection system based on the Internet of Things has been developed. This paper selects a MEMS high-precision pressure sensor with an accuracy of ±0.1 kPa to design an IoT sensing node based on the STM32L431 and LoRa/Wi-Fi 6, integrating pressure sensing and wireless communication. It proposes a sliding-average filtering and wavelet denoising algorithm, as well as a temperature-compensation calibration model, to optimize sensing accuracy. Additionally, it constructs a hierarchical early warning model based on pressure thresholds. The experiment demonstrates that the sensor achieves a detection accuracy of 99.2%, a response delay of less than 50 ms, a transmission packet loss rate of less than 0.5%, an end-to-end delay of less than 200 ms, and an early warning accuracy rate of 99.2% under battery overcharge/overtemperature conditions. The innovation of this study lies in the first integration of high-precision pressure sensing and IoT communication for battery protection. A low-power IoT sensing node tailored for battery aging scenarios has been designed, validating the novel application value of IoT sensing in the safety monitoring of new energy equipment. This system fills a gap in IoT pressure-sensing technology for battery protection, enabling practical applications and serving as a reference for implementing integrated sensing and communication technology.

This research study involves designing and optimizing a sandwich structure based on an auxetic structure to protect the pouch battery system for electric vehicles undergoing ground impact load. The core of the sandwich structure is filled with the auxetic structure that has gone through optimization to maximize the specific energy absorbed. Its performance is analyzed with the non-linear finite element method. Five geometrical variables of the auxetic structures are analyzed using the analysis of variance and optimized using Taguchi’s method. The optimum control variables are double-U hierarchal (DUH), the cross-section’s thickness = 2 mm, the length of the cell = 10 mm, the width of the cell = 17 mm, and the bending height = 3 mm. The optimized geometries are then arranged into three different sandwich structure configurations. The core is filled with optimized DUH cells that have been enlarged to 200% in length, arranged in 11 × 11 × 1 cells, resulting in a total dimension and mass of 189 × 189 × 12 mm and 0.75 Kg. The optimized sandwich structure shows that the pouch battery cells can be protected very well from ground impact load with a maximum deformation of 1.92 mm, below the deformation threshold for battery failure.

Improvement in electric vehicle technology requires the lithium-ion battery system’s safe operations, protecting battery fire damage potential from road debris impact. In this research a design of sandwich panel construction with a lattice structure core is evaluated as the battery protection system. Additive manufacturing technology advancements have paved the way for lattice structure development. The sandwich protective structure designs are evaluated computationally using a non-linear dynamic finite element analysis for various geometry and material parameters. The lattice structure’s optimum shape was obtained based on the highest Specific Energy Absorption (SEA) parameter developed using the ANOVA and Taguchi robust design method. It is found that the octet-cross lattice structure with 40% relative density provided the best performance in terms of absorbing impact energy. Furthermore, the sandwich panel construction with two layers of lattice structure core performed very well in protecting the lithium-ion NCA battery in the ground impact loading conditions, which the impactor velocity is 42 m/s, representing vehicle velocity in highway, and weigh 0.77 kg. The battery shortening met the safety threshold of less than 3 mm deformation.

The market for electric vehicles (EVs) has been growing in popularity, and by 2027, it is predicted that the market valuation will reach $869 billion. To support the growth of EVs in public road safety, advances in battery safety research for EV application should achieve low-cost, lightweight, and high safety protection. In this research, the development of a lightweight, crashworthy battery protection system using an excellent energy absorption capability is carried out. The lightweight structure was developed by using metastructure constructions with an arrangement of repeated lattice cellular structures. Three metastructure configurations (bi-stable, star-shaped, double-U) with their geometrical variables (thickness, inner spacing, cell stack) and material types (stainless steel, aluminium, and carbon steel) were evaluated until the maximum Specific Energy Absorptions (SEA) value was attained. The Finite Element Method (FEM) is utilised to simulate the mechanics of impact and calculate the optimum SEA of the various designs using machine learning methodology. Latin Hypercube Sampling (LHS) was used to derive the design variation by dividing the variables into 100 samples. The machine learning optimisation method utilises the Artificial Neural Networks (ANN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to forecast the design that produces maximum SEA. The optimum control variables are star-shaped cells consisting of one vertical unit cell using aluminium material with a cross-section thickness of 2.9 mm. The optimum design increased the SEA by 5577% compared to the baseline design. The accuracy of the machine learning prediction is also verified using numerical simulation with a 2.83% error. Four different sandwich structure configurations are then constructed using the optimal geometry for prismatic battery protection subjected to ground impact loading conditions. An optimum configuration of 6×4×1 core cells arrangement results in a maximum displacement of 7.33 mm for the prismatic battery in the ground impact simulation, which is still less than the deformation threshold for prismatic battery safety of 10.423 mm. It is shown that the lightweight metastructure is very efficient for prismatic battery protection subjected to ground impact loading conditions.

The growing adoption of electric vehicles (EVs) has intensified the need for efficient, intelligent, and grid-independent Bi-directional charging systems. Conventional EV charging solutions heavily rely on grid electricity, leading to high energy costs, grid instability, and low renewable energy utilization. Existing Bi-directional charging systems often lack real-time prioritization of energy sources, fail to optimize solar and energy storage system (ESS) usage, and do not incorporate adaptive control mechanisms for varying grid conditions. To address these gaps, this study proposes an Energy Management Controller (EMC) for Bi-Directional EV Charging, integrating a prioritized solar to ESS to grid energy distribution strategy to maximize renewable energy usage while ensuring system stability and cost efficiency. The proposed EMC is implemented on an ESP32 microcontroller and manages energy flow via a 6-channel relay module. A temperature-based safety mechanism is embedded to prevent overheating, shutting down relays if the system temperature exceeds 50°C. The control logic dynamically adjusts power flow based on grid stress levels, solar irradiance, ESS state of charge (SOC), and EV battery SOC. The system is monitored using ThingsBoard for real-time visualization and InfluxDB for historical data analysis. Experimental validation across 12 predefined operational scenarios demonstrated that the EMC effectively reduces grid dependency to 15%, achieves renewable energy utilization of up to 90%, and maintains a fast relay switching response time of 50ms. The safety mechanism successfully prevents overheating, ensuring reliable operation under all test conditions.

The exponentially growing electrification market is driving demand for lithium-ion batteries (LIBs) with high performance. However, LIB thermal runaway events are one of the unresolved safety concerns. Thermal runaway of an individual LIB can cause a chain reaction of runaway events in nearby cells, or thermal propagation, potentially causing significant battery fires and explosions. Such a safety issue of LIBs raises a huge concern for a variety of applications including electric vehicles (EVs). With increasingly higher energy-density battery technologies being implemented in EVs to enable a longer driving mileage per charge, LIB safety enhancement is becoming critical for customers. This comprehensive review offers an encompassing overview of prevalent abuse conditions, the thermal event processes and mechanisms associated with LIBs, and various strategies for suppression, prevention, and mitigation. Importantly, the report presents a unique vantage point, amalgamating insights sourced not only from academic research but also from a pragmatic industrial perspective, thus enriching the breadth and depth of the information presented.

A nonlinear dynamic structural optimization method is presented for the design of electric vehicles. A pack crush test and a pole impact test are selected as two different types of battery pack safety assessment. Two finite element models are defined for the pack crush test and the pole impact test, and two optimization problems are formulated for each test, respectively. The battery pack is the shared part of the two finite element models. The equivalent static loads method is employed for the nonlinear dynamic response optimization of the multi-model. The current equivalent static loads method can consider only one model while the current multi-model optimization is only for linear response optimization. A novel equivalent static loads method is proposed to handle multiple finite element models by using multi-model optimization. The mass of the structure is minimized, and displacement constraints are defined on the intrusion of the battery pack to prevent fire in the analyses. The resultant design can protect the battery system from physical shocks and car accidents.