Search Results (193)

Many electric vehicle (EV) batteries will retire in the next 5–10 years around the globe. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. The second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESSs) for the electric grid, which will also create a robust circular economy for EV batteries. This article proposes a two-layered energy management algorithm (monthly layer and daily layer) for demand charge reduction for an industrial consumer using photovoltaic (PV) panels and BESSs made of retired EV batteries. In the proposed algorithm, the monthly layer (ML) calculates the optimal dispatch for the whole month and feeds the output to the daily layer (DL), which optimizes the BESS dispatch, BESSs’ degradation, and energy imported/exported from/to the grid. The effectiveness of the proposed algorithm is tested as a case study of an industrial load using a real-world demand charge and Real-Time Pricing (RTP) tariff. Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer. Full article

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO₂ emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance. Full article

Recently, electric vehicles (EVs) have proven to be a practical option for lowering greenhouse gas emissions and reducing reliance on fossil fuels. Lithium-ion batteries, at the core of this innovation, require efficient thermal management to ensure optimal performance, safety, and durability. This article reviews current scientific studies on controlling the temperature of lithium-ion batteries used in electric vehicles. Several cooling strategies are discussed, including air cooling, liquid cooling, the use of phase change materials (PCMs), and hybrids that combine these three types of cooling, with the primary objective of enhancing the thermal performance of the batteries. Additionally, the challenges and proposed solutions in battery pack design and energy management methodologies are explored. As the demand for electric vehicles increases, improving battery thermal management systems (BTMSs) is becoming increasingly important. Implementing and developing better BTMSs will help increase the autonomy and safety of electric vehicles in the long term. Full article

Enhanced Battery Management Systems (BMS) are essential for improving operational efficacy and safety within Electric Vehicles (EVs), especially in tropical climates where traditional systems encounter considerable performance constraints. This research introduces a novel two-tiered deep learning framework that utilizes a two-stage Long Short-Term Memory (LSTM) framework for precise prediction of battery voltage and SoC. The first tier employs LSTM-1 forecasts individual cell voltages across a full-scale 120-cell Lithium Iron Phosphate (LFP) battery pack using multivariate time-series data, including voltage history, vehicle speed, current, temperature, and load metrics, derived from dynamometer testing. Experiments simulate real-world urban driving, with speeds from 6 km/h to 40 km/h and load variations of 0, 10, and 20%. The second tier uses LSTM-2 for SoC estimation, designed to handle temperature-dependent voltage fluctuations in high-temperature environments. This cascade design allows the system to capture complex temporal and inter-cell dependencies, making it especially effective under high-temperature and variable-load environments. Empirical validation demonstrates a 15% improvement in SoC estimation accuracy over traditional methods under real-world driving conditions. This study marks the first deep learning-based BMS optimization validated in tropical climates, setting a new benchmark for EV battery management in similar regions. The framework’s performance enhances EV reliability, supporting the growing electric mobility sector. Full article

This paper presents a novel adaptive cell recombination strategy for balancing lithium-ion battery packs, targeting electric vehicle (EV) applications. The proposed method dynamically adjusts the series–parallel configuration of individual cells based on instantaneous state of charge (SoC) and load demand, without relying on conventional DC-DC converters or passive components. A hardware-efficient switching topology using SPDT (Single Pole Double Throw) switches enables flexible recombination and fault isolation with minimal complexity. The control algorithm, implemented in MATLAB/Simulink, evaluates multiple cell-grouping configurations to optimize balancing speed, energy retention, and operational safety. Simulation results under charging, discharging, and resting conditions demonstrate up to 80% faster balancing compared to sequential methods, with significantly lower component count and minimal energy loss. Validation using Panasonic NCR18650PF cells confirms the model’s real-world applicability. The method offers a scalable, high-speed, and energy-efficient solution for integration into next-generation battery management systems (BMS), achieving performance gains typically reserved for more complex converter-based architectures. Full article

In this study, in order to overcome the limitations of existing electric vehicle (EV) thermal management systems (TMS), a highly integrated and coordinated operation strategy for EV thermal management was proposed. Specifically, an integrated architecture with a 10-way valve was established to replace traditional 3-way and 4-way valves to enhance the coupling between coolant circuits. Six operating modes were realized via the switching function of the 10-way valve, including the mode of waste heat recovery. A highly integrated TMS model was developed on the AMEsim2304 platform, followed by parameter matching. The accuracy of the model was validated through comparative analysis with laboratory and environmental chamber test results. Based on the designed highly integrated TMS, a classical fuzzy Proportional-Integral-Derivative Control (PID) control strategy was introduced to regulate the coolant circulation pump. Simulation analyses and experimental results demonstrated that the optimized system could reduce the battery pack heating time by approximately 300 s compared to the pre-optimized configuration. Moreover, the waste heat recovery could improve the cabin heating rate from 1.9 °C/min to 3.4 °C/min, representing a 43.7% enhancement. Furthermore, the output power of the high-pressure liquid heater remained low, resulting in a 10% reduction in overall heating energy consumption. Based on simulation and experimental analyses, this research can promote the progress of thermal management system technology for electric vehicles to a certain extent. Full article

The development of vehicle-to-grid (V2G) technique and the growth of battery swapping stations are expected to enhance the resilience of power networks. However, V2G battery swapping stations exhibit inconsistencies among internal battery packs, where the power capacity is significantly affected by the battery swapping behavior of electric vehicle (EV) users. To address this issue, this paper proposes a secondary frequency control strategy for V2G battery swapping stations that accounts for battery pack heterogeneity. First, a user behavioral model is developed through quantitative analysis of key factors such as economic incentives, time costs, and battery degradation, which is then used to optimize the operation of V2G battery swapping stations. Moreover, active balancing of EV battery energy levels is achieved by incorporating penalty terms into the objective function. Finally, a distributed secondary frequency control strategy based on the consensus algorithm is established to minimize total frequency control loss. Simulation results demonstrate that the proposed strategy effectively meets the secondary frequency control requirements of the power grid. Full article

Batteries are essential for the functionality of electric vehicles (EVs), leading to their design with enhanced performance and durability. Consequently, traction batteries are often replaced while they still retain the properties for use in less stressful demanding applications, with lower power and storage requirements. This serves as a notable opportunity for circular economy. The energy management system presented is designed with lithium-ion batteries coming from EVs and repurposed for electricity storage as a smart backup solution for buildings. The system buys and stores energy from the grid during low-cost periods and utilizes the stored electricity to feed the demand, avoiding high electricity prices and smoothing out peak consumptions exceeding a predefined power limit. To illustrate the proposal, a case study is presented based on the Spanish market, analyzing the impact on the electricity savings for end consumers as well as the extended second-life estimation for a pack of batteries. The analysis of the results will help assess if the system is both economically feasible and environmentally sustainable from a circular economy point of view. Full article

The adoption rate of battery electric vehicles (EVs) is rapidly increasing. Electric vehicles differ significantly from conventional internal combustion engine vehicles and vary widely across different manufacturers. Emergency responders (ERs) and recovery personnel may have less experience with EVs and lack timely access to critical information such as the extent of the stranded energy present, high-voltage safety hazards, and post-crash handling procedures in a user-friendly manner. This paper presents a software/hardware interactive tool named Electric Vehicle Information for Incident Response Solutions (EVIRS) to aid in the quick access to emergency response and recovery information. The current prototype of EVIRS identifies EVs using the VIN or Make, Model, and Year, and offers several useful features for ERs and recovery personnel. These features include integration and easy access to emergency response procedures tailored to an identified EV, vehicle structural schematics, the quick identification of battery pack specifications, and more. For EVs that are not severely damaged, EVIRS can perform calculations to estimate stranded energy in the EV’s battery and discharge time for various power loads using either EV dashboard information or operational data accessed through the CAN interface. Knowledge of this information may be helpful in the post-crash handling, management, and storage of an EV. The functionality and accuracy of EVIRS were demonstrated through laboratory tests using a 2021 Ford Mach-E and associated data acquisition system. The results indicated that when the remaining driving range was used as an input, EVIRS was able to estimate the pack voltage with an error of less than 3 V. Conversely, when pack voltage was used as an input, the estimated state of charge (SOC) error was less than 5% within the range of 30–90% SOC. Additionally, other features, such as retrieving emergency response guides for identified EVs and accessing lessons learned from archived incidents, have been successfully demonstrated through EVIRS for quick access. EVIRS can be a valuable tool for emergency responders and recovery personnel, both in action and during offline training, by providing crucial information related to assessing EV/battery safety risks, appropriate handling, de-energizing, transport, and storage in an integrated and user-friendly manner. Full article

Lithium-ion battery cells are the fundamental components of all Energy Storage Systems (ESSs) used in electric vehicles (EVs). Increasing concerns about safety issues, particularly the response of battery cells to mechanical crushes that can lead to internal short circuits (ISCs) and potential thermal runaway (TR), necessitate detailed investigation. To evaluate the response of a battery under abuse conditions, a homogeneous finite element (FE) model of a battery cell was developed. This model employs a simplified representation of a battery cell where the internal properties are assumed to be uniform throughout the entire cell. A full factorial approach was utilized to determine the homogenized jellyroll material characteristics. A detailed FEM serves as a benchmark for validating the homogeneous battery model. While requiring less computational effort, the homogeneous model maintains sufficient accuracy, making it suitable for modelling entire battery packs, thanks to the reduced number of elements. Full article

The use of lightweight components in automobiles started a new chapter in the automotive sector due to the renewable energy and sustainability increasing the overall efficiency of vehicles. As vehicle weight is directly linked to energy consumption, reducing mass through advanced materials can significantly decrease energy usage and emissions over the vehicle’s lifetime. This present study aims to conduct a preliminary life cycle assessment (LCA) of a prototype battery pack manufactured using pultruded composite materials with a volume fraction of 50% glass fibers and a volume fraction of 50% nylon 6 (PA6) matrix by quantifying the CO₂ emissions and cumulative energy demand (CED) associated with each stage of the battery pack’s life cycle, encompassing production, usage, and end-of-life recycling. The results of the EuCia Eco Impact Calculator and from the literature reveal that the raw materials extraction and use phases are the most energy-intensive and contribute mainly to the environmental footprint of the battery pack. For a single battery pack for EV, the CED is 13,629.9 MJ, and the CO₂ eq emissions during production are 1323.9 kg. These results highlight the need for innovations in material sourcing and design strategies to mitigate these impacts. Moreover, the variations in recycling methods were assessed using a sensitivity analysis to understand how they affect the overall environmental impact of the system. Specifically, shifting from mechanical recycling to pyrolysis results in an increase of 4% to 19% of the total CO₂ emissions (kg CO₂). Future goals include building a laboratory-scale model based on the prototype described in this paper to compare the environmental impacts considering equal mechanical properties with alternatives currently used in the automotive industry, such as aluminum and steel alloys. Full article

Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead to more severe outcomes. Current regulations for post-crash fuel system integrity evaluation do not differentiate between EVs and ICE vehicles. However, the causes of fires in these vehicles differ due to variations in the design and construction of their fuel systems. This study analyzed seventeen cases of EV post-crash fires in Korea to derive two representative risk scenarios for EV post-crash fires. The first scenario involves significant intrusion into the EV front-end structure resulting from high-speed frontal collisions, while the second scenario involves direct impacts to the battery pack mounted under the vehicle from road curbs at low speeds (30–40 km/h). Based on these scenarios, we conducted tests to assess battery damage severity under two crash test modes, simulating both high-speed frontal collisions and low-speed curb impacts. The test results led to the development of a draft crash test concept to evaluate EV post-crash fire risks. Furthermore, we assessed the reproducibility of these test modes in relation to actual EV post-crash fires. Our findings indicate that square-shaped impactors provide higher reproducibility in simulating real EV post-crash fire incidents compared to hemisphere-shaped impactors. Additionally, a fire occurred 31 days after the storage of a crash-evaluated battery test specimen, which was determined to be caused by moisture invasion during post-crash storage, accelerating a micro-short circuit. This study aims to contribute to the development of new evaluation methods for the Korean New Car Assessment Program (KNCAP) to enhance EV post-crash fire safety by utilizing these test results to refine collision severity evaluation methods. Full article

Integrated thermal management system (ITMS) technology for electric vehicles (EV) has become a major industry research direction. However, R290 refrigerants are still not applied on a large scale in EVs. Therefore, we developed a suitable thermal management system for R290 in this study. This architecture adapts an unusual indirect design, which can coordinate the heat between the air conditioner, battery pack, and electric motor. We focused on heat pump air conditioning systems for EV thermal management; thus, we carried out the performance analysis of R290 under the cooling and heating conditions of our ITMS through an experimental approach. The current study explores various aspects affecting the performance of heat-pump air conditioners: refrigerant charge, electronic expansion valve (EXV) opening, compressor speed, and performance between R290 and R134a under different external temperatures. We aim to improve cooling and heating efficiencies. Among these parameters, the EXV opening and compressor speed have the greatest impact on the performance of the ITMS, as evidenced by the optimal EXV opening and lower compressor speed to maximize the coefficient of performance (COP) and increase the heat transfer rate. In addition, this study has shown that, compared to an ITMS equipped with R134a, R290 has a smaller refrigerant charge, better heat transfer rate and COP under heating conditions, and similar performance under cooling conditions. Full article

Amid concerns about environmental degradation and the consumption of non-renewable energy, the development of electric vehicles (EVs) has accelerated, with increasing focus on safety. On the road, battery packs are exposed to potential risks from unforeseen objects that may collide with or scratch the system, which may lead to damage or even explosions, thus endangering the safety of transportation participants. In this study, several predictive models aimed at assessing the safety performances of battery packs are proposed to provide a basis for data-driven structural optimization by numerically simulating the deformation of the battery base plate. Initially, a finite element model of the battery pack was developed, and the accuracy of the model was verified by performing modal analysis with various commercial software tools. Then, representative samples were collected using optimal Latin hypercube sampling, followed by collision simulations to gather data under different collision conditions. Next, the prediction accuracy of three models—PSO-BP neural network, RIME-BP neural network, and RBF neural network—was compared for predicting battery pack bottom shell deformation. Finally, the prediction accuracy of the models was compared based on error functions. The results indicate that these neural network models can accurately predict deformation under frontal collision conditions within the specified limits, with the RIME-BP model yielding the best performance beyond those limits. The developed neural network prediction model is able to accurately assess the mechanical response of battery packs under frontal collision, providing support for data-driven structural optimization. It also provides an important reference for improving the safety and durability of battery pack design. Full article

The transport sector is responsible for nearly a quarter of global CO₂ emissions annually, underscoring the urgent need for cleaner, more sustainable alternatives such as electric vehicles (EVs). However, the electrification of heavy goods vehicles (HGVs) has been slow due to the substantial power and battery capacity required to match the large payloads and extended operational ranges. This study addresses the research gap in battery pack design for commercial HGVs by investigating the electrical and thermal behaviour of a novel battery pack configuration using an electro-thermal model based on the equivalent circuit model (ECM). Through computationally efficient 1D modelling, this study evaluates critical factors such as cycle ageing, state of charge (SoC), and their impact on the battery’s range, initially estimated at 285 km. The findings of this study suggest that optimal cooling system parameters, including a flow rate of 18 LPM (litres per minute) and actively controlling the inlet temperature within ±7.8 °C, significantly enhance thermal performance and stability. This comprehensive electro-thermal assessment and the advanced cooling strategy set this work apart from previous studies centred on smaller EV applications. The findings provide a foundation for future research into battery thermal management system (BTMS) design and optimised charging strategies, both of which are essential for accelerating the industrial deployment of electrified HGVs. Full article