A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries

• Published in: Energy (Oxford) 2024-10, Vol.305, p.132395, Article 132395
• Main Authors: Ge, Dongdong, Jin, Guiyang, Wang, Jianqiang, Zhang, Zhendong
• Format: Article
• Language: English
• Subjects: algorithms; batteries; data collection; Electric vehicles; energy; energy density; Extreme learning machine; Lithium-ion batteries; Optimization mechanism; radar; State of charge; State of health
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2024.132395

Lithium-ion batteries (Libs) have become the best power source for electric vehicles due to their high energy density and long cycle life. The state estimation is directly related to the safe and efficient use of Libs. In our previous research, a Bat Algorithm-Extreme Learning Machine (BA-ELM) model was constructed and promising results were achieved for state of health (SOH) estimation. To increase the engineering application value of BA-ELM for SOH/state of charge (SOC) estimation, optimization mechanism of improving the global exploration and local exploitation capabilities of the model was studied. First, three improved methods including a conversion mechanism of Artificial Bee Colony algorithm employing bees and scout bees, a mechanism for adaptive adjustment of the search frequency of bats according to the Euclidean distance difference, and an adaptive inertia weight coefficient method were proposed to construct a novel Improved Bat Algorithm (IBA)-ELM model. Second, a battery life degradation test platform was built to obtain the degradation dataset for Libs. The variation rules of nine battery variables and cycles were revealed. Four high-quality SOH-related features were extracted. Thus, the SOH estimation performance of six models was evaluated. Third, a Libs charge–discharge dataset under driving conditions was collected and processed. Four models were trained based on 72,048 samples from four conditions. Further, SOC estimation tests were conducted on 35,071 samples from completely different conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of SOH estimation by IBA-ELM decreased by 21.93 % and 23.79 %, respectively, compared to those by BA-ELM. The RMSE and MAE of SOC estimation decreased by more than 10 %. Error, radar, and violin plots demonstrate that the IBA-ELM could accurately estimate SOH/SOC in larger scale datasets, simultaneously exhibiting better generalization performance. •The mechanism to improve and balance the global exploration and local exploitation capabilities of the model is studied.•The SOH dataset of degradation test for seven months and the dataset of 110,000 SOC samples are obtained and processed.•The variation rules between nine battery variables and cycles are revealed, respectively. Four high-quality SOH-related features are extracted.•The SOH/SOC estimation performance are systematically evaluated by using error index, radar, scatter, and violin plots.