Browse Topic: Energy storage systems
Heavy-duty vehicles, particularly those towing higher weights, require a continuous/secondary braking system. While conventional vehicles employ Retarder or Engine brake systems, electric vehicles utilize recuperation for continuous braking. In a state where HV Battery is at 100% of SOC, recuperated energy from vehicle operation is passed on to HPR and it converts electrical energy into waste heat energy. This study focuses on identification of routes which are critical for High Power Brake Resistors (HPRs), by analyzing the elevation data of existing charging stations, the route’s slope distribution, and the vehicle’s battery SOC. This research ultimately suggests a method to identify HPR critical vehicle operational routes which can be useful for energy efficient route planning algorithms, leading to significant cost savings for customers and contributing to environmental sustainability
The development and implementation of Lithium-ion (Li-ion) batteries, particularly in Automotive applications, requires substantial diagnostic and practical modelling efforts to fully understand the electrical and thermal characteristics in the batteries across various operating conditions. Electrical thermal modelling prompts the understanding of the battery electrical characteristics along with the thermal behavior beyond what is possible from experiments, and it provides a basis for exploring electrical and thermal management control strategies for batteries in electric vehicles (EVs). For replicating the electrical behavior of Li-ion batteries under varied operating situations, an equivalent circuit model (ECM) must be created. This model aids in forecasting the transient distribution of electrical and thermal properties at various operating states as well as estimating heat generation within the battery pack. The paper focus in the following application areas: An equivalent
Electrification in off-highway vehicles faces several challenges due to the unique requirements and operational features of heavy-duty applications. Key challenges include power demand, limited range, weight, size, and the costs associated with electrification. Lithium-based batteries have limited capacity and range, and heavy-duty operations can rapidly drain the battery's power. Managing battery power for these operations requires careful planning and optimization of the vehicle's energy consumption to ensure efficient utilization of the battery's capacity. In electric off-highway vehicles, the remaining battery discharge run-time is closely related to the management of operational applications in the field. The utilization of battery power for heavy operations can be enhanced by estimating battery run-time and run distance during operation, which can then be displayed on the vehicle’s display unit. This facilitates the operator's understanding of how much longer the battery can
Today's battery management systems include cloud-based predictive analytics technologies. When the first data is sent to the cloud, battery digital twin models begin to run. This allows for the prediction of critical parameters such as state of charge (SOC), state of health (SOH), remaining useful life (RUL), and the possibility of thermal runaway events. The battery and the automobile are dynamic systems that must be monitored in real time. However, relying only on cloud-based computations adds significant latency to time-sensitive procedures such as thermal runaway monitoring. Because automobiles operate in various areas throughout the intended path of travel, internet connectivity varies, resulting in a delay in data delivery to the cloud. As a result, the inherent lag in data transfer between the cloud and cars challenges the present deployment of cloud-based real-time monitoring solutions. This study proposes applying a thermal runaway model on edge devices as a strategy to reduce
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