A fuzzy neural network combines two separate ideas: a neural network’s ability to learn from data, and fuzzy logic’s ability to represent “somewhere between” states rather than forcing every input into a hard category. For SoC estimation this matters because the underlying voltage/current/temperature measurements are noisy and the true SoC-to-signal relationship is genuinely nonlinear and uncertain rather than crisply defined — a fuzzy layer lets the network express partial membership of several conditions at once instead of committing to a single hard output at every intermediate stage.
Like a plain BP or RBF network, a fuzzy neural network is trained from measured data rather than derived from a battery model, so it inherits the general strengths (no explicit model required, handles nonlinearity well) and weaknesses (needs representative training data, harder to interpret than a physics-based model) of the neural-network family. Reported implementations have achieved SoC estimation error below 1% in bench testing, though — as with any data-driven method — that figure is specific to the training and test conditions used and should not be read as a general guarantee.
References
- S. Zhai, Z. Ding, R. Hu, H. Lu, R. Jiang and Y. Zhang, “An Improved Fuzzy Neural Network Method for SOC Estimation of Lithium-ion Battery,” 2022 China International Conference on Electricity Distribution (CICED), Changsha, China, 2022
- Wen-Yeau Chang, The State of Charge Estimating Methods for Battery: A Review, ISRN Applied Mathematics, Volume 2013, Article ID 953792
Part of a series expanding on the SoC Estimation Techniques overview.