Quantum Computing-Based State-of-Charge Estimation of Electric Vehicle Battery

Most of the traditional approaches for state of Charge (SoC) estimation are marred with model uncertainty, sensor noise, and nonlinearities. This chapter introduces a novel quantum computing approach to SoC estimation with the Qiskit framework to map classical battery parameters onto quantum states. A parameterized Ry gate is utilized to map classical values like voltage, nominal capacity, load resistance, and time onto the rotation angles that describe the charge state of a battery. The quantum states so obtained are mapped to the Q-sphere and state vector representations, which provide some indication of the probabilistic nature of quantum estimation.The proposal provides the basis under which hybrid quantum-classical battery management systems could have performed better than traditional models under certain constraint. The chapter makes a reference to battery electrochemistry and quantum information science in casting a futuristic vision towards the application of quantum computation to smart energy networks.