Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.

An accurate control-oriented electrothermal model is of great importance for onboard temperature monitoring and efficient performance management of lithium (Li)-ion batteries in automobile applications. This paper presents a control-oriented electrothermal model for pouch-type electric vehicle batteries. This model uses the Chebyshev-Galerkin (CG) approximation method and captures the heat generation of positive and negative tabs, the heat flow between the tabs and the body, and the uneven heat generation inside the battery. This model consists of two lumped-mass sub-models for positive and negative tabs and a two-dimensional (2-D) CG sub-model for the main battery body. The heat generation in the 2-D CG model is strongly dependent on the electrical parameters that are conversely functions of battery temperature. The lumped-mass models are decoupled from the 2-D CG model and parameterized separately by the particle swarm optimization algorithm and validated against the temperature measurements of a 20-Ah pouch-type lithium iron phosphate (LFP-G) battery. The results demonstrate that the coupled model accurately predicts the temperatures of the tabs and the temperature distribution inside the battery. Besides, the computational complexity of the coupled model is also evaluated, and the result shows that the model has great potential for real-time temperature monitoring and efficient thermal management.

Integration of energy management strategies (EMSs) with intelligent transportation systems exhibits a promising way to use the energy-saving potential of hybrid electric vehicles (HEVs). Battery, engine, cabin, and their heating and cooling systems are examples of dynamic thermal loads that typically exist during HEV operation. These loads are variable and highly influenced by ambient temperature and driving conditions. The primary objective of this paper is to develop a decentralized health-consciousness learning-based integrated thermal and energy management (ITEM) system that, besides minimizing fuel consumption, considers driver comfort and battery lifetime in the context of speed and temperature preview. In this regard, a multi-agent deep reinforcement learning (MADRL) framework with long short-term memory (LSTM) is proposed to consider the distinct dynamic characteristics of the ITEM system as well as capture the influence of past observations. A high-fidelity power-split HEV model established by Autonomie is utilized to implement and analyze the performance of the proposed approach. Based on the results, the multi-agent method outperforms both the rule-based and single-agent strategies. The MADRL agent can reduce battery degradation by 48% compared to rule-based while keeping the cabin temperature in the comfort zone. Furthermore, the proposed multi-agent strategy is implemented in a hardware-in-the-loop (HIL) system to evaluate its efficacy in real-world scenarios. Our experimental results demonstrate that the battery temperature and cabin temperature curves closely match the simulation results, with a maximum absolute error of 0.45 and 0.85, respectively.