Lithium-ion batteries are an attractive power source in many scenarios. In some particular cases, including providing backup power for drones, frequency modulation, and powering electric tools, lithium-ion batteries are required to discharge at a high rate (2~20 C). In this work, we present a method to estimate the state of health (SOH) of lithium-ion batteries with a high discharge rate using the battery’s impedance at three characteristic frequencies. Firstly, a battery model is used to fit the impedance spectrum of twelve LiFePO₄ batteries. Secondly, a basic estimation model is built to estimate the SOH of the batteries via the parameters of the battery model. The model is trained using the data of six batteries and is tested on another six. The RMS of relative error of the model is lower than 4.2% at 10 C and lower than 2.8% at 15 C, even when the low-frequency feature of the impedance spectrum is ignored. Thirdly, we adapt the basic model so that the SOH estimation can be performed only using the battery’s impedance at three characteristic frequencies without having to measure the entire impedance spectrum. The RMS of relative error of this adapted model at 10 C and 15 C is 3.11% and 4.25%, respectively.

A conventional electric vehicle (EV) powertrain relies on a centralized high-voltage DC–AC inverter, thereby limiting cell-level control and potentially reducing overall driving range and battery lifetime. This paper studies an H-bridge-based cell-level inverter topology that performs power conversion at the cell level, enabling independent control of individual cells and expanding the design space for battery management. Leveraging these additional degrees of freedom, we propose a model-agnostic energy-throughput control strategy that extends EV range while improving battery-pack lifetime.Because usable energy (and thus driving range) and lifetime are governed by the cells with the lowest state-of-charge (SOC) and state-of-health (SOH), respectively, the proposed controller preferentially routes energy throughput to healthier cells. Specifically, during charging, it permits cell SOCs to diverge to promote SOH equalization; during discharging, it rebalances SOC to maximize usable capacity under per-cell constraints. The proposed SOC–SOH-aware control strategy is evaluated on two aging models representing lithium manganese oxide and lithium iron phosphate chemistries, using a Tesla Model 3 charge–discharge profile across 14 different parameter settings. Simulations show a 7--38\% improvement in lifetime relative to a conventional SOC-only balancing baseline. More broadly, the results suggest a software-defined pathway to extend EV pack life through routine charging, with minimal reliance on specific degradation models or discharge profiles.