Battery Lifetime Prediction via Neural Networks with Discharge Capacity and State of Health

The market share of electric vehicles (EVs) has grown exponentially in recent years to reduce air pollution and greenhouse gas emissions. principal part an EV is the energy storage system, which usually batteries. Thus, accurate estimation remaining useful life (RUL) batteries, for optimal health management a decision-making policy, still remains challenge automakers. In this paper, problem battery RUL prediction studied from new perspective. Unlike other strategies existing literature, proposed technique uses intelligent lifespan lithium–iron–phosphate (LFP) batteries via modified version neural networks. It data-driven approach optimization method does not require any prior comprehension initialization parameters model. To validate verify technique, we use LFP data sets, experimental results showed that methodology can well learn characteristic relationship discharge capacities as its state (SOH), where cycle changes ages with time cycles.