Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces a joint charge and thermal management problem for batteries in a battery–supercapacitor hybrid por source of a portable system, which has been equipped with a forced convection cooling technique, such as a fan. A key consideration in such a system is that the battery aging depends strongly on the battery temperature, which is in turn a function of the workload running on the device and the control policy for the fan. More precisely, this paper presents a hierarchical algorithm for maximizing the battery lifespan under given workload conditions. The algorithm relies on a combination of reinforcement learning and dynamic programming techniques. Simulation results show that the proposed algorithm achieves up to    $2 {\times } $    improvements in battery lifespan, resulting in completion of up to 80% additional workload before the battery expires.
['Qing Xie', 'Donghwa Shin', 'Naehyuck Chang', 'Massoud Pedram']
I am writing a research paper on a idea Joint Charge and Thermal Management for Batteries in Portable Systems With Hybrid Power Sources
[{"from": "human", "value": "I am writing a research paper on a idea Joint Charge and Thermal Management for Batteries in Portable Systems With Hybrid Power Sources"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  introduces a joint charge and thermal management problem for batteries in a battery\u2013supercapacitor hybrid por source of a portable system, which has been equipped with a forced convection cooling technique, such as a fan. A key consideration in such a system is that the battery aging depends strongly on the battery temperature, which is in turn a function of the workload running on the device and the control policy for the fan. More precisely, this paper presents a hierarchical algorithm for maximizing the battery lifespan under given workload conditions. The algorithm relies on a combination of reinforcement learning and dynamic programming techniques. Simulation results show that the proposed algorithm achieves up to    $2 {\\times } $    improvements in battery lifespan, resulting in completion of up to 80% additional workload before the battery expires."}]