This paper presents the development of machine learning-enabled data-driven models for effective capacity predictions for lithium-ion batteries under different cyclic conditions. To achieve this, a model structure is first proposed with the considerations of battery ageing tendency and the corresponding operational temperature and depth-of-discharge. Then based on a systematic understanding of covariance functions within the Gaussian process regression, two related data-driven models are developed. Specifically, by modifying the isotropic squared exponential kernel with an automatic relevance determination structure, ‘Model A’ could extract the highly relevant input features for capacity predictions. Through coupling the Arrhenius law and a polynomial equation into a compositional kernel, ‘Model B’ is capable of considering the electrochemical and empirical knowledge of battery degradation. The developed models are validated and compared on the Nickel Manganese Cobalt Oxide (NMC) lithium-ion batteries with various cycling patterns. Experimental results demonstrate that the modified Gaussian process regression model considering the battery electrochemical and empirical ageing signature outperforms other counterparts and is able to achieve satisfactory results for both one-step and multi-step predictions. The proposed technique is promising for battery capacity predictions under various cycling cases.

Energy management is critical to reduce the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This paper proposes a novel knowledge-based, multi-physics-constrained energy management strategy for hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multi-constrained least costly formulation is proposed by augmenting the over-temperature penalty and multi-stress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.