Microprocessor Execution Time and Memory Use of Battery State of Charge Estimation Algorithms
2022-01-0871
03/29/2022
- Event
- Content
- Accurate battery state of charge (SOC) estimation is important for safe and reliable performance of electric vehicles (EVs). Lithium ion batteries, which are commonly used for EV applications, have strong time-varying and non-linear behavior, which makes SOC estimation challenging. In this paper, a processor on the loop (PIL) platform is used to assess the execution time and memory use of different SOC estimation algorithms. Three different SOC estimation algorithms are presented and benchmarked, including an extended Kalman filter (EKF), feedforward neural network (FNN), and a recurrent neural network with long short-term memory (RNN-LSTM). The algorithms are deployed to an NXP S32K1 microprocessor and executed in real time to assess the processor loading. The impact of the number of algorithm parameters on the performance of each algorithm is also investigated. In order to ensure the validity of running these models for multiple cells in the pack, the impact of increasing the number of instances to run each algorithm simultaneously is investigated as well. The results show that the EKF has the least execution time while the RNN-LSTM model has the highest execution time among the studied algorithms. The FNN SOC estimation model is found to be a promising alternative with a good trade-off between the accuracy and the computational load.
- Citation
- Naguib, M., Kollmeyer, P., Vidal, C., Duque, J. et al., "Microprocessor Execution Time and Memory Use of Battery State of Charge Estimation Algorithms ," SAE Technical Paper 2022-01-0871, 2022, .