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Precise and Robust SoC Estimation Using Adaptive Filters-Based Techniques for Electric Trucks Daimler Truck Innovation Center India Pvt. Limited
- Format:
- Book
- Conference/Event
- Author/Creator:
- Kumar, Raman, author.
- Conference Name:
- 11th SAEINDIA International Mobility Conference (SIIMC 2024) (2024-12-11 : New Delhi, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2024
- Summary:
- 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 operate BEV safely and efficiently battery operating conditions and health is of utmost importance.Battery management system (BMS) controller is needed for optimized and safe operation of high voltage (HV) battery. For correct behavior of BMS, accuracy of state of charge (SoC) estimation is important. SoC is an important and decisive factor for deciding operating limits such as current limits, voltage limits and battery operational range (charge-discharge interval). Inaccurate SoC estimation can accelerate battery aging and cause damage to it.The current state of art deploys coulomb counting technique for SoC calculation, this approach encounters 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 errors 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 against actual test data for real time correlation
- Notes:
- Vendor supplied data
- Publisher Number:
- 2024-28-0152
- Access Restriction:
- Restricted for use by site license
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