Kalman-based estimation of remaining Battery discharge run-time and run-distance for electrified off-highway Vehicle using Battery parameters

Kalman-based estimation of remaining Battery discharge run-time and run-distance for electrified off-highway Vehicle using Battery parameters Supriya Narwade, Tejal Sarda Abstract: Electrification in off-highway vehicles is facing several challenges due to the unique requirements and driving features of heavy-duty applications. A few key challenges are power demand, limited range, weight, size and cost of electrification. Lithium-based batteries have limited capacity and range, and heavy-duty operations can instantly drain the battery's power. Managing battery power for these operations requires careful planning and optimization of the vehicle's power consumption to ensure that the battery's capacity is utilized efficiently. In electric off-highway vehicles, remaining battery discharge run-time strongly relates to the management of driving the applications on the field. Here, utilization of battery power for heavy operations can be achieved by estimating battery run-time and run-distance during the operation and display it on Vehicle’s display unit. This facilitates operator to understand how much time the battery can be useful if same amount of load is continued in use. Similarly, if run-distance is known then with present load conditions, how much distance vehicle can travel is predicted. This helps in planning the activity for off-highway equipment. The paper explores the estimation of both the parameters by Moving Average Filter and Kalman estimation methods. Here, Battery parameters like Battery State of Charge, Battery Voltage, Instantaneous Battery Current and Actual Velocity are used. Results of both the methods are compared. After observing merits and demerits, Kalman based estimations results are found more appropriate with actual field data. Also, it is observed from results that Kalman estimation accounting dynamic load situations and instantaneous spikes on the vehicle. Indexed terms: Electrification, Off-highway vehicles, Battery power estimation, State of Charge, Estimation algorithms, Moving Average Filter, Kalman based estimation.