Battery management systems increasingly require accurate battery health prognostics under strict on-device constraints. This paper presents DLNet, a practical framework with dual-stage distillation of liquid neural networks that turns a high-capacity model into compact and edge-deployable models for battery health prediction. DLNet first applies Euler discretization to reformulate liquid dynamics for embedded compatibility. It then performs dual-stage knowledge distillation to transfer the teacher model's temporal behavior and recover it after further compression. Pareto-guided selection under joint error-cost objectives retains student models that balance accuracy and efficiency. We evaluate DLNet on a widely used dataset and validate real-device feasibility on an Arduino Nano 33 BLE Sense using int8 deployment. The final deployed student achieves a low error of 0.0066 when predicting battery health over the next 100 cycles, which is 15.4% lower than the teacher model. It reduces the model size from 616 kB to 94 kB with 84.7% reduction and takes 21 ms per inference on the device. These results support a practical smaller wins observation that a small model can match or exceed a large teacher for edge-based prognostics with proper supervision and selection. Beyond batteries, the DLNet framework can extend to other industrial analytics tasks with strict hardware constraints.

翻译：电池管理系统日益要求在严格的设备端约束下实现精确的电池健康预测。本文提出DLNet，一种实用的双阶段液态神经网络蒸馏框架，将高容量模型转化为紧凑且可部署于边缘设备的电池健康预测模型。DLNet首先应用欧拉离散化重新表述液态动力学以兼容嵌入式系统。随后执行双阶段知识蒸馏，迁移教师模型的时序行为，并在进一步压缩后恢复该行为。基于联合误差-成本目标的帕累托引导选择保留了在精度与效率间取得平衡的学生模型。我们在广泛使用的数据集上评估DLNet，并通过int8部署在Arduino Nano 33 BLE Sense上验证实际设备可行性。最终部署的学生模型在预测未来100个周期电池健康状态时实现0.0066的低误差，较教师模型降低15.4%。模型规模从616 kB压缩至94 kB（减少84.7%），在设备上每次推理耗时21毫秒。这些结果支持一种实用的“小模型胜出”现象：在恰当的监督与选择下，小模型在边缘预测任务中能够匹敌甚至超越大型教师模型。除电池领域外，DLNet框架可扩展至其他具有严格硬件约束的工业分析任务。