Junmin Wang

Battery charging protocols play a pivotal role in the efficiency, safety, and longevity of battery systems. Existing protocol generation methods face three key limitations, including limited protocol diversity, inflexible across application scenarios, and lack of a compact unified representation that enables systematic analysis. To address these challenges, this article proposes a variational autoencoder (VAE)-based universal battery charging protocol (UBCP) generation framework that learns a unified, low-dimensional, latent representation capable of expressing charging protocols with comprehensive patterns. A randomized dataset is used to train the encoder, ensuring exposure to diverse patterns necessary for learning such a universal representation. A merge-based data augmentation strategy is introduced for the decoder to fuse distinct base protocols, improving reconstruction accuracy while preserving the ability to generate novel protocol patterns. The framework is evaluated in protocol reconstruction, fast-charging, and power-constrained charging scenarios. Experimental results show that the model achieves high-fidelity reconstruction and efficiently customizes protocols to meet given constraints. Compared with optimization-based baselines, the UBCP framework attains comparable or superior performance while requiring only a simple search and constraint check, highlighting its flexibility and computational efficiency.

In conventional immersion and invariance (I&I) adaptive control design, control parameter adaptation is typically linear with respect to the parameter-error-induced perturbation, resulting in quadratic-rate dissipation of energy associated with the off-the-manifold variable. Departing from such a convention, this article contributes a novel strategy - polynomial adaptation. As the name suggests, control parameter adaptation in this approach takes the form of a general polynomial in relation to the perturbation. Accordingly, this new design induces polynomial-rate energy dissipation, which is faster than the quadratic one in the conventional scheme, thereby enhancing closed-loop control performance. The theoretical underpinnings of the new approach are demonstrated through the design of an I&I adaptive tracking control law for a general nth-order, single-input-single-output, parametrically uncertain, nonlinear system in the controllable canonical form. Additionally, a numerical study of the proposed method on the second-order forced Duffing oscillator showcases its improved transient performance in comparison to a baseline controller developed with the standard I&I adaptive control technique.

We introduce a new control framework crafted to enhance the transient performance of the adaptive vehicle lateral guidance system. This framework, for the first time, systematically and synergistically alloys two elements, variable-exponent Lyapunov function and attracting-manifold design, together. It offers a dual set of benefits. Firstly, we propose a novel nonquadratic Lyapunov function design with a variable exponent. Compared to conventional quadratic Lyapunov function-based designs, this approach accelerates the dissipation of tracking-error energy globally, thus fostering improved transient performance. Secondly, the attracting-manifold design provides a noncertaintyequivalence method to further enhance transient performance by promptly mitigating the perturbation induced by parametric uncertainty. This strategic fusion leverages the strengths of both elements, elevating transient performance beyond what either design could achieve independently. The new control framework is established in a general context, making it applicable to a class of control-affine systems in Brunovsky's canonical form. We apply it to develop an adaptive vehicle lateral guidance system and show its improved transient performance through experiments conducted on a high-fidelity, moving-base, driving simulator.