The thermal effect has a significant impact on the performance of a lithium-ion (Li-ion) battery. Thus, modeling the thermal process, which always involves unknown boundary heat exchange, is significant to battery management. Two critical issues should be addressed for the thermal process modeling: 1) the nominal model which is constructed offline can be updated efficiently to compensate for any online disturbances, and 2) the influence of previous and recent spatiotemporal dynamics may be varying and should be handled properly. Bearing these in mind, a spatial correlation based incremental learning technique is designed for spatiotemporal modeling. First, the incremental learning technique is developed to update the dominant spatial basis functions (DSBFs) of the nominal model, which is constructed by a time/space separation based method. Then, a forgetting factor is incorporated into the incremental learning technique to handle time-varying dynamics. Additionally, the popular approximator, that is, the radial basis function neural network, is utilized to identify the low-dimensional temporal model. Simulations and experiments on a pouch type battery with boundary heat exchange have demonstrated the accuracy and efficiency of the proposed modeling method.

Temperature distribution of Lithium-ion battery (LIB) belongs to a nonlinear distributed parameter system (DPS), which is infinite dimensional in space direction. Modeling of such system often leads to the following challenges: 1. Time/space coupled dynamics; 2. Time-varying dynamics; 3. Spatial nonlinearity. For the first two challenges, Karhunen-Loeve (KL) decomposition based data-driven spatiotemporal models have been widely researched and successfully applied to industrial thermal processes. However, this method is a global linear model reduction method that ignores the nonlinear properties of measurements. This will prohibit the model performance of strong nonlinear DPS, which has strong spatial nonlinearity refers to the aforementioned challenge 3. To address this problem, a local properties embedding based modeling method is developed for nonlinear DPS. Firstly, finite dimensional nonlinear spatial basis functions (BFs) that can representative the nonlinear feature of original space are learned by local properties embedding based method. Then, the low dimensional representative can be derived using time/space separation and the unknown temporal coefficients can be identified through traditional neural learning algorithm. Finally, the spatiotemporal temperature distribution can be reconstructed by time/space synthesis. The proposed modeling method can be more effective than traditional KL based method for modeling nonlinear DPS.

The temperature monitoring is indispensable to the optimal and safe operation of the Lithium-ion battery. In this paper, a spatiotemporal learning model designed by evolutionary algorithm is proposed to predict the thermal distribution. To formulate the multi-characteristic spatial dynamics, the chicken swarm optimization based fusion of different dimensionality reduction methods is proposed for learning spatial basis functions. Through integration with the time/space separation based approach and equivalent circuit model based thermal model, the reduced-order model is derived. The related parameters of the reduced-order model are identified by integrating chicken swarm optimization with time/space separation based approach. A Bayesian regularized neural network based compensation model is developed to compensate for the model errors caused by the spatiotemporal coupled dynamics. Based on the Rademacher complexity, the generalization bound of the proposed model is analyzed. Simulations and comparisons demonstrate the superiority of the proposed model.

Global principal component analysis (PCA) has been successfully introduced for modeling distributed parameter systems (DPSs). In spite of the merits, this method is not feasible due to parameter variations and multiple operating domains. A novel multimode spatiotemporal modeling method based on the locally weighted PCA (LW-PCA) method is developed for large-scale highly nonlinear DPSs with parameter variations, by separating the original dataset into tractable subsets. This method implements the decomposition by making full use of the dependence among subset densities. First, the spatiotemporal snapshots are divided into multiple different Gaussian components by using a finite Gaussian mixture model (FGMM). Once the components are derived, a Bayesian inference strategy is then applied to calculate the posterior probabilities of each spatiotemporal snapshot belonging to each component, which will be regarded as the local weights of the LW-PCA method. Second, LW-PCA is adopted to calculate each locally weighted snapshot matrix, and the corresponding local spatial basis functions (SBFs) can be generated by the PCA method. Third, all the local temporal models are estimated using the extreme learning machine (ELM). Thus, the local spatiotemporal models can be produced with local SBFs and corresponding temporal model. Finally, the original system can be approximated using the sum form of each local spatiotemporal model. Unlike global PCA, which uses global SBFs to construct a global spatiotemporal model, LW-PCA approximates the original system by multiple local reduced SBFs. Numerical simulations verify the effectiveness of the developed multimode spatiotemporal model.

Lithium-ion batteries are widely used as power sources in industrial applications. Electrochemical models and simulations are crucial to disclose many details that cannot be directly measured through experiments. Parameter identification of an accurate electrochemical model is much more cost-effective than direct and destructive measurement methods. However, the complex structure and strong nonlinearity of electrochemical models will make the parameter identification very difficult. Additionally, time-consuming electrochemical simulations can significantly limit the identification efficiency. This article proposes a surrogate-model-based scheme to achieve high-efficiency parameter identification of an electrochemical battery model. To be specific, the proposed method is implemented by the close integration of an evolutionary algorithm and a surrogate model. A sensitivity-based identification strategy is first designed to alleviate the difficulty of optimization. Then, a surrogate model is developed from historical data to gradually approach the objective function used for parameter evaluations. Finally, an evolutionary algorithm is employed to find promising solutions by minimizing the output of the surrogate model. Simulations and experimental studies demonstrate the effectiveness and high efficiency of the proposed method.