Kalman-Based Estimation of Remaining Battery Discharge Run-Time and Run-Distance for Electrified off-Highway Vehicle Using Battery Parameters

<div class="section abstract"><div class="htmlview paragraph">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 remain useful under the same load. Similarly, if the run distance is known, the predicted distance the vehicle can travel under current load conditions can be calculated. This information aids in planning activities for off-highway equipment. The paper explores the estimation of both parameters using Moving Average Filter and Kalman estimation methods. Key battery parameters, such as Battery State of Charge, Battery Voltage, Instantaneous Battery Current, and Actual Vehicle Speed, are utilized. The results of both methods were compared, revealing that Kalman-based estimation is more accurate with actual field data. It also effectively handles dynamic load situations and instantaneous spikes in the vehicle.</div></div>