Precise and robust SoC estimation using adaptive filters-based techniques for Electric Trucks

The traction for zero emission vehicles in the transportation industry is creating a focus on Battery Electric vehicles (BEV) as one of the potential alternate powertrain sources. To the relevance of electric trucks, the range estimation and optimum energy prediction & utilization brings utmost importance. Battery management system (BMS) controller is needed for optimized and safe operation of high voltage (HV) battery. For correct behavior of battery management system, it is important to accurately estimate the state of charge (SoC). SoC is an important and decisive factor for deciding operating limits such as current limits, voltage limits & battery operational range (charge-discharge interval). Inaccurate State of Charge (SoC) estimation can accelerate battery aging and cause damage to components, affecting energy throughput. Current state of art deploys coulomb counting technique for SoC calculation, this approach encounters the challenges like sensor noises and initial SoC error (carried from the previous charge-discharge cycle). This paper mainly focuses on exploring various techniques to minimize the error in the SoC calculation. Extensions of Kalman filters are modelled, parameterized, and studied to negotiate the limitations of present SoC estimation techniques. Further, robustness against interference noise and initial error is carried out to ensure the filter performance. These simulation results are validated to actual test data for real time correlation. Keywords: Electric Trucks, Battery management system (BMS), State of Charge (SoC) estimation, Coulomb counting, Adaptive filters-based techniques.