Browse Topic: Battery thermal management
Battery Thermal Management Systems (BTMS) play a critical role in ensuring the longevity, safety, and efficient operation of lithium-ion battery packs. These systems are designed to better dissipate the heat generated by the cells during vehicle operation, thereby maintaining a uniform temperature distribution across the battery modules, preventing overheating and mitigating the chances of thermal runaway. However, one of the primary challenges in BTMS design lies in achieving effective thermal contact between the battery cells and the cooling plate. Non-uniform or excessive application of Thermal Interface Materials (TIMs) without ensuring robustness and uniformity can increase interfacial thermal resistance, leading to significant temperature variations across the battery modules, which may trigger power limitations via the Battery Management System (BMS) and these thermal changes can cause inefficient cooling, ultimately affecting battery performance and lifespan. In this paper, a
India's electric 2-wheeler (E2W) market has witnessed fast growth, driven by lucrative government policies. The two-wheeler segment dominates the Indian automotive market, accounting for the largest share of total sales. Consequently, the manufacturers of 2-wheelers are developing new electric vehicles (EV) tailored for the Indian market. However, the Indian EV market has witnessed multiple fire accidents in recent years, raising safety concerns among consumers and industry stakeholders. These incidents highlight key weakness in battery thermal management systems (BTMS), particularly during charging. Most existing E2W BTMS relies on passive (natural) air cooling, which has been associated with fire incidents due to its inefficiency in heat dissipation, particularly during charging in India's high-temperature environment. Therefore, it is imperative to build thermally viable and economical BTMS for the growing E2W vehicles with fast charging capability. FEV is actively developing the
The growth of the electric vehicle market has driven the advancement of technologies related to energy storage and lithium-ion cells, which stand out for their fast charge and discharge capabilities, high energy density, and long service life. This paper proposes a thermal control strategy for lithium-ion battery packs using the Active Disturbance Rejection Control (ADRC) method. The model is developed in Simcenter Amesim software, using cylindrical 21700 cells in a pack equipped with a water-cooling system, and was adapted for export in FMU format and integrated into MATLAB/Simulink, where the control algorithms were designed and simulated. From step input tests, a first-order transfer function was identified with a fitting of 97.67%, supporting the adoption of a first-order ADRC. The tests involved scenarios with changes in temperature reference and current disturbances typical of vehicle operation. Results indicate that ADRC performs satisfactorily in temperature tracking, even
Thermal Management System (TMS) for Battery Electric Vehicles (BEV) incorporates maintaining optimum temperature for cabin, battery and e-powertrain subsystems under different charging and discharging conditions at various ambient temperatures. Current methods of thermal management are inefficient, complex and lead to wastage of energy and battery capacity loss due to inability of energy transfer between subsystems. In this paper, the energy consumption of an electric vehicle's thermal management system is reduced by a novel approach for integration of various subsystems. Integrated Thermal Management System (ITMS) integrates air conditioning system, battery thermal management and e-powertrain system. Characteristics of existing integration strategies are studied, compared, and classified based on their energy efficiency for different operating conditions. A new integrated system is proposed with a heat pump system for cabin and waste heat recovery from e-powertrain. Various cooling
Modern battery management systems, as part of Battery Digital Twin, include cloud-based predictive analytics algorithms. These algorithms predicts critical parameters like Thermal runaway events, state of health (SOH), state of charge (SOC), remaining useful life (RUL), etc. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. This is a very critical and safety function and delay is not acceptable, but automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud and similarly delay in return of the detected warning to the driver back in the vehicle. As a result, the inherent lag in data transfer between the cloud and vehicles challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes application of Federated Learning and applying to a thermal runaway model in low
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