On-Device Personalized Charging Strategy With an Aging Model for Lithium-Ion Batteries Using Deep Reinforcement Learning

The charging protocol design is a control problem between charge time and capacity retention solved with numerous methods. However, the outcome is an optimal yet rigid charge protocol. Ever changing user behaviour limits the acceptability of one rigid optimal protocol to affect the growing market of LIBs - electric vehicles (EVs), embedded systems etc. It is imperative to redefine optimal charging by incorporating the user behaviour resulting in a dynamic charging strategy. We have formulated the personalized charging strategy problem as a Markov Decision Process (MDP). Q-learning and Deep Deterministic Policy Gradient (DDPG) method is used to solve the MDP. We then present a full spectrum of charging strategies based on perceived user requirement. Three representative charge protocols are demonstrated. The aggressive protocol can charge 77% SOC in ~15 minutes faster than MSCCCV (baseline). ~1.5 years of extra battery life is offered by the life-saver protocol which takes only 30 minutes more than MSCCCV to fully charge. The balanced protocol provides a quick boost and yet maintains a similar charge time and health as MSCCCV. We present an online methodology to retune the protocol on-device based on battery dynamics and user behavior. Finally, the claims are validated using real-world experiments. Note to Practitioners—This work was motivated by the problem of dynamic optimization of the battery-charging trajectory for each user of a mobile device or electric vehicle. The existing mechanism of an optimal charging problem revolves around finding a trade-off between charge time and battery health, not taking into account every user’s unique perspective. This offers only a fixed way of charging a device. This paper proposes to design an optimal charging protocol by taking user behaviour also into account. At each charging phase, the trajectory used for battery charging is optimized w.r.t expected charge time (user behaviour), battery health, and safety constraints of battery at that point of time. The method is trained to learn the battery ageing mechanism for individual user. Existing methodologies finds it tough to accommodate the process of optimal charging for fresh battery characteristics while also considering the constraints of an ageing battery. The proposed solution improves productivity because existing ways of optimizing require significant man-months. An automatic way of optimizing the charging protocol for each charging cycles is an efficient and reliable way to capture degradation as well as the battery-to-battery variability. The outcome of our work is a spectrum of charging protocols each optimized w.r.t battery characteristics and user expectations. Thus, each user can be catered to, with a unique charging protocol optimally designed for their immediate requirement. The practical limitation is to ensure the model converges during on-device retraining. The future direction of the work will be to incorporate more detailed battery model for optimizing the charging protocol.