ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State-of-Charge Estimation

• Published in: IEEE transactions on consumer electronics Vol. 70; no. 1; pp. 4311 - 4320
• Main Authors: Dong, Zhekang, Ji, Xiaoyue, Wang, Jiayang, Gu, Yeting, Wang, Junfan, Qi, Donglian
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Circuit design; Circuit synthesis; Data transmission; Electric charge; Electric vehicle; Electric vehicles; Energy management; Estimation; Feature extraction; Kalman filters; Lithium-ion batteries; Logic gates; Memristors; Modules; Neuromorphic computing; neuromorphic computing system; Neuromorphic engineering; Random noise; Rechargeable batteries; Residential energy; Robustness; Smart buildings; SOC estimation; State of charge
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2023.3257201

Accuracy and speed of Lithium-ion battery state of charge (SOC) estimation determine the reliability and stability of electric vehicle (EV), as well as promoting the development of smart home energy management system. Existing SOC estimation approaches embedded in commercial EVs still suffer from limitations of low precision, effectiveness, and relatively low robustness. To address these issues, we propose an internal cascaded neuromorphic computing system (ICNCS) via memristor circuits for EV SOC estimation. Specifically, we use three circuit modules to facilitate the design of the proposed ICNCS. Firstly, a bidirectional gated recurrent unit (Bi-GRU) circuit module is designed, enabling adequate feature extraction from the time-contextual battery information. Secondly, an attention circuit module is proposed to distinguish the useful and unimportant information related to SOC estimation. Thirdly, the Kalman filter module is constructed to eliminate the random noise caused by data transmission and processing. Finally, a series of experiments and analysis demonstrate that the proposed ICNCS has good performances in terms of accuracy (the lowest achievable RMSE and MAE are 0.853 and 0.711, respectively), time efficiency (approximately <inline-formula> <tex-math notation="LaTeX">16\sim 20 </tex-math></inline-formula> times), and robustness (anti-noise capacity), indicating an advancement in consumer electronics applications.