Lithium-ion battery development is conventionally driven by energy and power density targets, yet the performance of a lithium-ion battery pack is often restricted by its heat rejection capabilities. It is therefore common to observe elevated cell temperatures and large internal thermal gradients which, given that impedance is a function of temperature, induce large current inhomogeneities and accelerate cell-level degradation. Battery thermal performance must be better quantified to resolve this limitation, but anisotropic thermal conductivity and uneven internal heat generation rates render conventional heat rejection measures, such as the Biot number, unsuitable. The Cell Cooling Coefficient (CCC) is introduced as a new metric which quantifies the rate of heat rejection. The CCC (units W.K⁻¹) is constant for a given cell and thermal management method and is therefore ideal for comparing the thermal performance of different cell designs and form factors. By enhancing knowledge of pack-wide heat rejection, uptake of the CCC will also reduce the risk of thermal runaway. The CCC is presented as an essential tool to inform the cell down-selection process in the initial design phases, based solely on their thermal bottlenecks. This simple methodology has the potential to revolutionise the lithium-ion battery industry.

Knowledge of remaining battery charge is fundamental to electric vehicle deployment. Accurate measurements of state-of-charge (SOC) cannot be obtained directly and estimation methods must be used instead. This requires both a good model of a battery and a well-designed state estimator. Here, hysteretic reduced-order battery models and adaptive extended Kalman filter estimators are shown to be highly effective, accurate predictors of SOC. A battery model parameterisation framework is proposed, which enhances standardised methods to capture hysteresis effects. The hysteretic model is parameterised for three independent NMC811 lithium-ion cells and is shown to reduce voltage RMS error by 50% across 18 h automotive drive-cycles. Parameterised models are used alongside an extended Kalman filter, which demonstrates the value of adaptive filter parameterisation schemes. When used alongside an extended Kalman filter, adaptive covariance matrices yield highly accurate SOC estimates, reducing SOC estimation error by 85%, compared to the industry standard battery model.

Lithium-ion battery research has historically been driven by power and energy density targets. However, the performance of a lithium-ion cell is strongly influenced by its heat generation and rejection capabilities which have received less attention. The development of adequate thermal metrics able to capture the anisotropic thermal conductivity and uneven internal heat generation rates characteristic of lithium-ion cells is therefore paramount. The Cell Cooling Coefficient (CCC), in W.K⁻¹, has been introduced as a suitable metric to quantify the rate of heat rejection of a given cell and thermal management method. However, there is no standardised methodology defining how to measure the heat generation capabilities of a cell. In this study, we applied the CCC empirical methodology to evaluate the rates of irreversible heat generation at various operation conditions, providing maps which give a complete insight into cell thermal performance. The maps derived show how the most important operational variables (frequency, C-rate, SOC and temperature) influence the cell thermal performance. These maps can be used along with the CCC by pack engineers to optimise the design of thermal management systems and to down select cells according to their thermal performance.