Autonomous driving paper index
Multi timescale predictive energy management for battery life extension in electric vehicles
One-line summary
Abstract Electric-vehicle battery energy management increasingly requires coordinated control of electrical demand, thermal behavior, and long-term degradation to ensure safe and durable operation.
Engineering notes
Under Urban-Nominal operation, it reduces RMS current to 125.75 A and achieves the lowest cumulative degradation among the predictive controllers.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract Electric-vehicle battery energy management increasingly requires coordinated control of electrical demand, thermal behavior, and long-term degradation to ensure safe and durable operation. Existing predictive and digital-twin-inspired model-based observer battery-management approaches often do not fully integrate fast electro-thermal regulation with slow health-aware supervisory adaptation. To address this gap, this study proposes a multi-timescale health-resilient predictive energy-management framework that combines a fast predictive control layer for real-time traction-demand satisfaction with a slow supervisory layer that updates health-dependent limits, adaptive weights, and operating envelopes using cumulative electro-thermal-aging stress. The framework is evaluated in discrete-time simulation under Urban-Nominal, Highway-Nominal, Aggressive-Hot, and Aged-Battery-Hot scenarios against Rule-Based, Fast-MPC-Only, and Electro-Thermal-MPC strategies. Results show that the proposed controller consistently provides the most favorable battery-preservation tradeoff. Under Urban-Nominal operation, it reduces RMS current to 125.75 A and achieves the lowest cumulative degradation among the predictive controllers. Under Aggressive-Hot operation, it lowers RMS current to 120.44 A and cumulative degradation to $$\:1.4929\times\:{10}^{-5}$$ , while under Aged-Battery-Hot conditions it again yields the lowest degradation and loss-energy trends among the predictive methods. The proposed framework therefore offers a balanced compromise between short-term energy-management performance and long-term battery durability, suggesting its potential usefulness for health-aware EV battery energy-management studies, subject to further experimental and long-horizon validation.
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